10 AI Engineering Principles in 2025

1. Models (Foundation Model Selection and Usage)

Choosing the right model is one of the most important decisions you’ll make as an AI engineer.

In 2025, you have access to powerful foundation models from labs like OpenAI (GPT-4o), Anthropic (Claude 3.7), Google (Gemini 2.5), and Meta (Llama 3), as well as open-source communities. Each model has trade-offs — some are better at reasoning, some respond faster, others are cheaper to run. Great engineers know how to pick the right model for the job.

Start by experimenting. Use different playgrounds and APIs. See how each model handles instructions. Pay attention to parameters like temperature, max tokens, and system instructions — they have a big impact on output quality.

‍

Key trade-offs to manage:

• Capability vs. speed. Higher-end models (like GPT-4) often produce more accurate or detailed results but with increased latency and cost, while smaller or distilled models respond faster. Use a powerful model for complex reasoning and a lighter model for simple tasks or real-time needs.
• Specialization. Use domain-specific models when available, for example, code-tuned models for programming help or medical-tuned models for healthcare data.
• Open-source vs. proprietary. Open-source models (like Llama 2) give you more control and privacy, but often need more engineering effort. Proprietary models (like the ones produced by OpenAI, Anthropic, or Google) are easier to use, better out of the box, but can be costly or limited by API terms.
• Scaling costs. If you’re doing fewer than 1K prompts per day, cost likely isn’t an issue. But if you scale, orchestration platforms like nexos.ai can help you switch between models, reduce downtime, and cut expenses.
• Future-proofing. The model landscape changes fast. Keep an eye on announcements (e.g., Google’s Gemini models or OpenAI’s new updates) and design your stack to be modular so you can swap models as better options become available.

‍

Example use cases:

• Use Gemini 2.5 Pro for legal document analysis (accuracy-critical) or a customer support chatbot (a 1M-token context window for extensive documentation).
• Use GPT-4o-mini for a real-time chat widget (low latency).
• Use Llama-2 locally for an offline-capable summarization tool.
• Combine models. For example, route easy queries to a small model and escalate tougher ones to a larger model.

‍

Notable tools/platforms:

• Hugging Face Hub: Explore and deploy open-source models with benchmark comparisons and one-click hosting.
• OpenAI Playground: Test prompts, tweak parameters, and see how OpenAI models behave.
• Anthropic Claude Console: Test prompts on Claude variants in-browser (perfect for iterating conversational flows and assessing model behavior).
• LangChain: Build LLM pipelines, chain together prompts, and integrate external tools (APIs, databases) in Python or JavaScript.
• LlamaIndex: Connect LLMs to your own data sources (documents, APIs).
• Vertex AI: Train, deploy, and monitor large models like Gemini or PaLM with enterprise-grade tooling and workflows.
• AWS Bedrock: Access multiple foundation models (Anthropic, AI21, Amazon Titan) with pay-per-use pricing and VPC support.
• Cohere: Fast, low-latency APIs for text generation, embeddings, and classification.
• Banana.dev: Run private, GPU-backed instances of open-source models with automatic scaling and minimal setup.

‍

Recommended resources:

• SMOL.AI newsletter: Industry updates.
• Applied LLMs: Free course collective for advanced engineers and tech executives.
• Hugging Face’s Open LLM Leaderboard: Model benchmarks.
• Latent Space: Leading AI engineering community.
• AI engineering summit: Leading AI engineering conference.
• Data scientist vs. AI engineer: Valuable explainer from IBM.

‍

People to follow:

• Andrej Karpathy, Ex-Tesla/OpenAI
• Dario Amodei, CEO of Anthropic
• Harrison Chase, CEO of Langchain
• Jonathan Ross, CEO of Grok
• Sam Altman, CEO of OpenAI
• Clem Delangue, CEO of Hugging Face
• Michael Truell, CEO of Cursor

2. Context and Retrieval (Context Windows and RAG)

LLMs can’t memorize everything — they rely on context. Think of an LLM’s context window as its short-term memory. It “sees” only the input text you send with a request. If that text exceeds the model’s limit (e.g., 128k tokens for GPT-4 Turbo or 1M tokens for GPT-4.1 mini), earlier parts get forgotten.

‍

That’s where RAG — retrieval-augmented generation — comes in. RAG gives the model access to external knowledge, so it doesn’t need to “remember” everything.

How RAG works:

(1) Ingestion. Stores your documents in a retrievable format (usually embeddings in a vector database).  

(2) Retrieval. When a user asks a question, the RAG system retrieves the most relevant pieces of information from the documents.  

(3) Response. It adds those chunks to the prompt so the LLM can use them to generate its answer. 

‍

Here’s a short comparison:

By 2025, RAG is everywhere: from customer support bots that look up policy documents to coding assistants that fetch API docs relevant to your question. Mastering it means you can build systems that function beyond the knowledge cutoff or limited training data of the model.

‍

Key practices:

• Use vector databases. Convert your documentation into embedding vectors and store them in a vector database (Pinecone, Weaviate, FAISS, etc.). At query time, you’ll embed the user’s query and find which document vectors are semantically the most similar. This way, you’ll find candidate text chunks likely to contain the answer.
• Add keyword indexing. You can also use keyword search technology (like Elasticsearch, Vespa, or even a simple inverted index) to retrieve relevant text. In some cases, combining keyword filtering with vector reranking can improve results.
• Chunk smart. Don’t just split your documents on character count. Use semantic chunking (by paragraph, header, or section) to preserve meaning. Tools like Chunkviz can help visualize your chunks and their content.
• Don’t overload the prompt. More isn’t always better. Quality drops when you stuff too much into the context window. Focus on the top 3–5 most relevant chunks. Focus on precision of retrieval over sheer quantity (a great tutorial on this topic).
• Prompt clearly. Once you have the retrieved context, integrate it into the model prompt clearly. For example: 

You are a QA assistant. Use the following context to answer, and if it's not helpful, say you don't know.
Context:
{retrieved text 1}
{retrieved text 2}
User question: {question}

‍

By delineating the context, you help the model separate provided info from its own knowledge. Also, instruct it on how to handle missing info.

• Preprocess queries. If the user asks two things at once ("Which shirts are best sellers, and are they in stock?"), split them. You could have the model rephrase the queries or detect the conjunction and treat the query as two separate ones (a great tutorial on this topic here). Some RAG systems do this automatically.
• Garbage in, garbage out. Perhaps the most important tip: audit your source data. A flawed knowledge base will lead to confidently wrong answers, so make sure what you're retrieving is accurate, current, and relevant. Have processes to update the knowledge store (for instance, re-index documents when policies change).
  
  

Example use cases:

• Customer support agent (Langchain + LangSmith)
  Build an agent that uses a vector-backed SQLite travel database to handle user queries. It can look up policy info, book flights or hotels, confirm details, and route to domain-specific subflows. Every model call and tool action is traceable with LangSmith for full visibility and debugging

‍

• Local research assistant (LangChain + Tavily + Ollama)
  Set up a local LLM pipeline that fetches documents via the Tavily API, deduplicates and chunks them, then summarizes key points using a local model like LLaMA 3.2. Add LangSmith tracing to track results and ensure reproducibility, even without an internet connection.
  

‍

• Personal assistant with tool use (LangChain + Gemini)
  Use LangChain agents and Google Gemini’s function calling to create a multi-purpose assistant. It can pull data from APIs like exchange rates or BigQuery, track session memory, handle multi-turn logic, and validate steps with human-in-the-loop when needed.
  
  

Key tools/platforms:

• Vector databases:‍
  
  • Pinecone: Fully managed, production-ready, and built for speed. ‍
  • Weaviate: Open-source and modular, with support for hybrid search (vector + keyword), GraphQL API, and schema-based modeling.‍
  • Chroma: Lightweight and easy to run locally. Perfect for rapid prototyping of RAG apps.‍
  • FAISS: A high-performance library from Meta for custom vector search and clustering.‍
  • ‍Milvus: Open-source, GPU-accelerated, and ready for large-scale workloads.
    

• Search indexes:
  
  • Elasticsearch / OpenSearch: Battle-tested full-text search engines that now support vector search — great for hybrid queries and scalable indexing.
  • Algolia: Fast, user-friendly, and relevance-focused. Built-in typo tolerance, faceting, and filtering make it a go-to for polished UX.
    
• RAG frameworks:
  
  • LangChain: A developer framework for chaining LLM calls, managing prompts, and integrating custom retrieval tools and APIs.
  • LlamaIndex: Index builder for custom data retrieval and reasoning.
    
• Full-stack RAG platforms:
  
  • Haystack (deepset): End-to-end toolkit for building production QA and search systems, handling ingestion, retrieval, and LLM integration.
  • Vespa: A large-scale engine for real-time serving of search, recommendation, and ML models. Offers both structured and vector retrieval.
    
• Data processing tools:
  
  • BeautifulSoup: A Python library for parsing HTML/XML and scraping web content into clean text for downstream indexing.
  • Apache Tika: A content analysis toolkit that extracts text and metadata from PDFs, Word docs, and other file formats for ingestion.
    

Recommended resources:

• Full Stack Retrieval series by Greg Kamradt
  A free guide covering RAG fundamentals, retrieval frameworks, evaluation techniques, and hands-on code examples across tutorials and videos. 
• Jason Liu’s RAG posts
  Articles on systematically improving RAG: query segmentation, specialized indices, metadata use, and feedback loops to boost relevance and precision. 
• Vespa engineering blog
  In-depth posts on building large-scale search and retrieval systems with Vespa, including hybrid semantic/lexical search, multi-vector indexing, and performance optimizations. 
• Haystack documentation
  Official Deepset Haystack docs and tutorials for building RAG pipelines: install guides, data ingestion, retriever configuration, prompt design, and generator integration. 
• LangChain documentation
  LangChain’s conceptual and tutorial pages on RAG applications: indexing data, building retriever/chain components, combining prompts, and customizing retrieval-augmented workflows. 

3. Fine-Tuning (Custom Training and Adaptation)

Fine-tuning lets you take a general-purpose LLM and train it to work better for your specific use case. In 2025, it’s more accessible than ever. You can fine-tune all model weights (full fine-tuning) or use lightweight techniques like LoRA to adjust just a small number of parameters.

Fine-tuning is what gives a model your brand's tone, handles industry-specific jargon, or nails a formatting style that prompting alone can’t consistently achieve. Most major APIs now support fine-tuning — including OpenAI (with GPT-3.5 Turbo and GPT-4.1 nano) — so you don’t need to host your own infrastructure to get started.

‍

Key concepts:

• Instruction vs. task tuning. Want better formatting, tone, or structure? Fine-tune on examples of desired behavior. Want the model to understand complex legal or financial content? Fine-tune on domain-specific data.
• Parameter-efficient tuning (PEFT). Techniques like LoRA (low-rank adaptation) let you train just a small adapter layer instead of updating the entire base model. It’s faster, cheaper, and avoids erasing the model’s general knowledge. You can adapt a 30B model with just a few million trainable parameters.

When to fine-tune. Rule of thumb: start with a prompt (cheap), add RAG when context overflows, and fine‑tune only when the cost of repeating yourself beats the cost of training.

• Safety and evaluation. Fine-tuning can improve performance or break it. Always test the fine-tuned version against your baseline model to ensure the benefits are real. Watch out for overfitting, regressions, or introduced biases.
• Cost considerations. Fine-tuning large models can be expensive. Many platforms charge by the token. But once fine-tuned, you may save money at inference with shorter prompts and faster outputs. Also consider tuning smaller models to get acceptable results at a fraction of the cost.
  
  

Example use cases:

• Fine-tune an LLM on your company’s customer support transcripts so your model mirrors the company’s tone and understands industry-specific terms. 
• Train a model on fantasy fiction to act as a lore-accurate NPC dialogue generator for a role-playing game.
• Use LoRA to adapt an open-source model for multilingual support without retraining the whole model (handy for localization).
• Instruction-tune an AI assistant to always start responses with a bullet summary. This is something hard to achieve consistently with prompts alone, but it becomes more reliable after fine-tuning with many examples in the desired format.
  
  

Notable tools/platforms:

• Hugging Face transformers and PEFT libraries. Fine-tune or apply LoRA adapters to open-source models with relatively few lines of code. The Hugging Face Hub even offers hosted auto-training for some models.
• OpenAI fine-tuning API. As of mid-2025, you can fine-tune models like GPT-4-1-nano directly via API. Just upload your data, and OpenAI handles the training. 
• MosaicML (Databricks) and Azure Machine Learning. Enterprise-scale platforms for fine-tuning large models. MosaicML gives you efficient training libraries and GPU-optimized workflows. Azure offers managed fine-tuning for OpenAI models, integrated into the Azure ecosystem.
• Colab and Kaggle notebooks. Perfect for lightweight prototyping. Community notebooks let you fine-tune models like T5 or GPT-J on shared GPUs — useful for testing ideas before committing to a bigger setup.
  
  

Recommended resources:

• OpenAI Forum – “Fine-tuning updates: Reinforcement fine-tuning now available + GPT-4.1 Nano fine-tuning” (May 14, 2025)
  Official release notes covering reinforcement fine-tuning for GPT-4o-mini (up to 40% gain on reasoning benchmarks) and supervised fine-tuning for GPT-4.1 Nano — a faster, more affordable path to customized models.
• Fine-tuning chapters (Hugging Face course)
  A step-by-step tutorial on fine-tuning transformer models, including the Trainer API for custom dataset preparation.
• Trelis Research – “Reinforcement Learning for LLMs in 2025” (Feb 2025)
  A 74-minute YouTube tutorial on advanced reinforcement learning strategies for LLMs, RLHF foundations, advanced algorithms like PPO and GRPO on benchmarks such as GSM8K and ARC, integration of tool-calling into RL pipelines, and hands-on code examples via the ADVANCED-fine-tuning repo.
• Google Cloud Blog – “Fine Tuning Large Language Models: How Vertex AI Takes LLMs to the Next Level” (Apr 6, 2024)
  Official end-to-end tutorial from Google showing how to fine-tune LLMs on your data, including data preparation, model training with the Vertex AI Python SDK, evaluation with built-in metrics, and deployment through Vertex AI Pipelines, Model Registry, and Endpoints.

4. Tools (Extending LLMs with Tool Use & APIs)

Modern AI models can do more than just chat – they can invoke tools and APIs to take actions or fetch information. In 2025, this means calling APIs, querying databases, performing calculations, executing backend tasks, and more — all based on structured outputs from the model. 

Tool use is what turns a model from a passive assistant into an interactive agent. You define what the model can do, and it decides when to do it.

‍

Key concepts:

• Function calling and plugins. In mid-2023, OpenAI introduced function calling, which lets you define functions the model can invoke. The model responds with structured output (e.g., JSON with function name + arguments), which your system executes and returns results back into the model’s context. OpenAI’s ChatGPT plugins and Advanced Data Analysis (formerly Code Interpreter) are examples where the model can use tools like web browsers and code runners. Anthropic’s Claude also has a tool use API.
• Grounding outputs. By giving the model tools, you ground it in reality. Instead of guessing answers, the model can fetch real-time data or perform exact calculations. This improves accuracy and reduces hallucinations, especially for math, facts, and time-sensitive info.
• Tight interfaces. Keep the tools the AI can access fairly narrow and well-defined. Models get the information about available tools from the descriptions you provide, so good function descriptions and examples improve reliability. Validate every input before executing a tool to avoid unexpected behavior.
• Error handling. If a tool fails (e.g., invalid input or API timeout), return the error as context so the model can try a different approach. This creates a feedback loop where the model can refine its plan.
• Security and limits. Limit which tools you allow. Use rate limits and sandboxing (especially for code execution). Monitor for infinite loops or excessive tool use. In production, you may constrain the number of tool calls per user query or have a watchdog for unusual behavior.
  
  

Example use cases:

• Database lookup bot. When asked something like “What’s my order status?”, the model calls a query_db(order_id=...) function to fetch live order data, then responds using that info.
• Email scheduling assistant. When a user says "Book a meeting with John next week," the model calls a calendar API (schedule_meeting(date, participant)) and confirms once it's done.
• Math solver. The model parses a word problem and invokes a calculate(expr) function instead of relying on the model’s imperfect math.
• News reader. If a user asks for today’s news, the model uses a search_web(query) tool to pull fresh content and summarize it. This is how Bing’s Chat and other connected assistants work, blending LLM reasoning with live data.
• E-commerce agent. The model uses check_inventory() or issue_refund() functions based on the user’s query, pulling real data and triggering backend workflows. For example, a user says they dislike a product color, and the assistant LLM produces a tool call output to issue_refund(customer_id=..., order_id=..., reason="color not as expected"). The final answer to the user would then be based on the outcome of that function (e.g., confirming the refund was processed).
  
  

Notable tools/platforms:

• OpenAI function calling. This function enables structured function calls via GPT-4 and GPT-4.1 models. The model’s output includes a {"function_call": ...} JSON when it wants to use one.
• LangChain and LangSmith. LangChain makes it easy to define and connect tools to LLMs. It handles orchestration, parsing, and response formatting. LangSmith, its companion, gives you debugging and observability for LLM calls and tool invocations.
• Claude’s Tool Use (Anthropic). Claude’s API supports tool calling directly. On desktop, Claude can interact with local “MCP servers” — integrations for apps like Slack or GitHub using a standardized interface. 
• Microsoft’s Semantic Kernel and Python AgentToolkits. These libraries are built for teams creating tool-using agents. They help structure decision-making (what to call and when) and support integration with OpenAI and other models. 
• Plugin ecosystems (e.g., ChatGPT + Zapier). While not always developer-facing, plugin platforms show what’s possible when you give models broad tool access safely. With ChatGPT and Zapier, you can connect to thousands of apps and trigger actions from natural language.
  
  

Recommended resources:

• OpenAI – Function Calling Guide
  A step-by-step breakdown of how to define tool schemas (name, parameters, descriptions), detect when the model wants to call a tool, execute it, and feed the results back into the chat flow.
• Anthropic – Tool Use Documentation
  Covers how Claude handles tool calls, including setup, response formatting, pricing, and how to link models to services using the Model Context Protocol (MCP).
• “LLM Orchestrator” by Navveen Balani (Jun 22, 2024)
  A practical guide to building modular AI workflows using function orchestration, plan→act→observe loops (e.g., ReAct), and structured API selection for real-world use.
• Youtube: “Function Calling with OpenAI APIs” (Jun 2024)
  A community tutorial of how to wire up a simple weather tool using OpenAI's function calling, including prompt design, API calls, and graceful error handling.
• YouTube: “What is Tool Calling?” by IBM Technology (Jan 17, 2025)
  An engineering-level deep dive video on tool invocation patterns, retry logic, and how tool calling reduces hallucination by anchoring the model in external systems.

People to follow:

• Hamel Husain, Parlance Labs, ex-Airbnb and Github
• Doug Safreno, CEO of Gentrace
• Jason Lopatecki, CEO of Arize AI
• Haroon Choudery, CEO of Autoblocks
• Eugene Yan, ML at Amazon, ex-Alibaba
• Shreya Shankar, PhD, UC Berkeley (AI, DB, HCI)

5. Prompting (Crafting Effective Prompts)

Prompting is how you communicate with a model — it’s the input that drives everything else. By 2025, prompt engineering is a well-defined skill, but it’s not about secrets and tricks. The core principle: clarity and context trump cleverness. Treat the model like a smart but literal intern — it will do what you say, not what you mean. The more deliberate your input, the better your output. Then there are proven techniques to further improve prompt performance: from giving examples to structuring the prompt to managing the conversation context.

‍

Key concepts:

• Clear instructions. Avoid vague or convoluted language. Say what you mean, as simply as possible. Instead of “User requires assistance regarding product use”, say “Help the user use the product.” If your prompt looks like a tangle of legalese, rewrite it.
• Prompt structure:
  
  • Instruction: What you want the model to do (“Summarize this email in one sentence” or “Translate to French”).
  • Context: Background info (“This is an email from a customer about a return policy”) or supporting data.
  • Input (if separate from instruction): The primary content to act on (e.g., the email text to summarize).
  • Format: Describe how the output should look (“Respond in JSON with fields X, Y” or “Give the answer and a one-line explanation”).
• Few-shot examples. Showing the model what you want often works better than telling it. A few well-placed input/output examples can guide classification, formatting, or reasoning far more effectively than a wall of instruction. Examples are essentially demonstrations – they prime the model to respond in a similar way.

Prompt to perform sentiment analysis:

I like this // Positive
I hate this // Negative
Wow that car was lit // Positive
What a horrible movie! //

‍

Output:

Negative

‍

• Techniques like chain-of-thought. Adding “Let’s think step by step” or formatting your prompt to encourage intermediate reasoning helps with logic-heavy tasks. There are even agent frameworks that automatically wrap your prompt to induce this (e.g., adding “Thought:” and “Final Answer:” to the structure).

• Structured prompts and outputs. Models respond well to structured formats — that keeps responses clean and easy to integrate with code. It also constrains the model, reducing rambling or hallucinated extras. For example, delineate sections like “Context:,” “Task:,” and “Output format:” explicitly in the prompt. Similarly, ask for output in a structured form where applicable (JSON, HTML, etc.).

Prompt:

**Context:**  
You are reviewing the notes from a project kickoff meeting.


**Meeting Notes:**  
- Date: 21 May 2025  
- Participants: Alice, Bob, Carol  
- Discussion:  
  - Alice to draft the product requirements document.  
  - Bob to set up the staging environment by next Wednesday.  
  - Carol to research competitor pricing and share findings.  
  - Follow-up meeting scheduled for 28 May 2025 at 10 AM.


**Task:**  
Extract all action items, including the task description, assignee, and due date.


**Output Format (JSON):**  
```json
{
  "action_items": [
    {
      "task": "<description>",
      "assignee": "<name>",
      "due_date": "<YYYY-MM-DD>"
    }
    // …additional items
  ]
}

‍

Output:

{
  "action_items": [
    {
      "task": "Draft the product requirements document",
      "assignee": "Alice",
      "due_date": "2025-05-28"
    },
    {
      "task": "Set up the staging environment",
      "assignee": "Bob",
      "due_date": "2025-05-28"
    },
    {
      "task": "Research competitor pricing and share findings",
      "assignee": "Carol",
      "due_date": "2025-05-28"
    }
  ]
}

‍

• Iterative prompt development. Prompt design is trial and error. Start simple, test, tweak, and repeat. Small changes can shift performance (like replacing a single word or adding a line like “If you don’t know, say ‘I don’t know’”). Always test edge cases and unexpected inputs.
  
  

Example use cases:

• Customer support Q&A:

**Instruction:**  
You are a helpful support agent.
**Context:**  
[Insert relevant company policy pages or extracted snippet here]
**Input:**  
Customer’s question: “<User’s question goes here>”
**Output Format:**  
- Respond in a polite tone.  
- If multiple steps are required, present them as a numbered list.

‍

• Data extraction: If you need to extract people and dates from text, giving an example of an input and the expected JSON output format can improve accuracy.

Prompt:

**Instruction:**  
You are a data extraction assistant.
**Input:**  
Alice Johnson’s meeting was set for 2023-11-30.  
Michael Lee was invited on December 1, 2022.
**Task:**  
Read the text above and extract each person’s name and associated date.
**Output Format (JSON):**  
```json
{
  "records": [
    {
      "name": "<Person’s full name>",
      "date": "<YYYY-MM-DD>"
    }
    // …additional records
  ]
}

‍

Output:

{
  "records": [
    {
      "name": "Alice Johnson",
      "date": "2023-11-30"
    },
    {
      "name": "Michael Lee",
      "date": "2022-12-01"
    }
  ]
}

‍

• Creative writing: Even creative tasks benefit from clarity. For a story generation, you can use a specific prompt like the one below. The result is much more on target than a generic “tell a fun story.”

Prompt:

**Instruction:**  
You are a creative writer.


**Task:**  
Write a short story in the style of Dr. Seuss.


**Constraints:**  
- Include a cat.  
- Include a talking toaster.  
- End with a clear moral.  
- Use rhyming couplets throughout.


**Output:**  
Provide the complete story as a single narrative of rhyming couplets.

‍

• Multi-turn conversation system: Use system-level prompts (if the platform allows) to set overarching behavior. Then in user prompts, maintain consistency by reminding context. Maintaining some persistent instruction ensures the model stays in character and context across turns.

Prompt:

**System Prompt:**  
You are an AI tutor who always Socratically guides the student—ask questions that lead them to discovery rather than simply giving answers.


**Conversation History:**  
**Student:** I’m struggling to understand basic integration.  
**Tutor:** What does taking the derivative of a function tell you about its original form?  
**Student:** It shows the rate of change of that function.


**User (Current Turn):**  
Given your previous explanation about antiderivatives, how would I approach integrating \(x^2\)?


**Output Format:**  
- Ask a leading question to prompt the student’s thinking.  
- Offer a hint rather than the full solution.  
- Keep the tone friendly and encouraging.

‍

Notable tools/platforms:

• PromptLayer. A prompt content management system that lets teams version, organize, and A/B test prompts outside their codebase. Includes analytics, evaluation pipelines, and role-based collaboration.
• PromptOps. DevOps for prompt workflows. Offers prompt version control, automated regression testing, real-time monitoring, and CI/CD integration, so you can manage prompts like production code.
• LangChain. A developer framework for managing dynamic prompts as templates (with Jinja2 or f-strings), chaining LLM calls, injecting retrieval, and executing tool calls — all in a unified flow.
• Jinja2. A Python templating engine used to programmatically generate prompts with user-specific inputs and contextual data.
• Guidance (Microsoft). A domain-specific library for writing structured prompts in code, enforcing schemas, control flow (loops, conditionals), and reusable components. It helps ensure your LLM outputs follow precise formats.
  
  

Recommended resources:

• OpenAI Function Calling Guide
  The official resource for defining function schemas and structured prompts that allow the model to send JSON or API calls, parse arguments, and integrate external tools.
• Prompt Engineering Guide (DAIR.AI)
  An open-source handbook covering prompt fundamentals, advanced techniques (few-shot, CoT, structured outputs), and best practices for managing and scaling prompts.
• PromptingGuide.ai
  A searchable collection of prompt patterns, examples, and model-specific tips.
• Prompting Fundamentals and How to Apply Them Effectively by Eugene Yan.
  A concise guide of what makes a good prompt work. Includes before-and-after examples that show how small changes improve consistency and accuracy.
• Prompt Tuning Playbook by Varun Godbole et al.
  A technical guide to optimizing prompts post-training for Gemini models. Covers structure, iteration tactics, and system prompt tuning.
• OpenAI Cookbook
  An extensive collection of API examples and techniques: from few-shot classification and structured outputs to function calling and code generation.

(And remember: prompt engineering is as much art as science. Keep experimenting!)

6. Orchestration and Agents (Multi-Step AI Workflows)

When you want an AI system to do more than just answer a question — when it needs to make decisions, call tools, or follow steps toward a goal — you’re working with agents and orchestration. An agent has some autonomy to decide how to achieve a goal — it plans and invokes tools or steps accordingly, as opposed to following a fixed workflow with predefined steps.

In 2025, the hype around AutoGPT and BabyAGI has settled into something more useful: agents with a purpose. These are focused systems that plan, act, and adapt within clearly defined boundaries. The principle is simple: use agents when flexibility is required. Otherwise, stick with deterministic flows.

Agents are already showing strong results in lead generation and sales (autonomous outreach, follow-ups) and in software development (code generation and refactoring). Voice agents handling inbound calls — answering questions, booking appointments, providing estimates — are proving especially valuable in areas like healthcare and home services. In coding, systems like Cursor’s Composer or Cognition Labs’ Devin can generate multi-file projects, run tests, and open pull requests with minimal input.

AI agents vs. agentic AI: know the difference

• AI agents are task-specific helpers designed to execute instructions. They handle things like drafting emails, summarizing docs, or booking meetings. They don’t learn or self-direct.
• Agentic AI are fully autonomous systems that perceive, decide, act, and learn. Think self-driving cars or real-time logistics optimization. They set their own goals and operate in complex environments.

Don’t confuse the two. Doing so leads to poor architecture decisions, unsafe deployments, and inflated expectations for what your system can achieve.

‍

Key concepts:

• When to use agents. Use agents when the right sequence of actions depends on the input. For example, “plan a trip” could involve many steps in different orders. But “summarize a PDF, then email it” doesn’t need decision-making — it needs a clear script. 
• Plan-act-observe loop. Most agents operate in cycles: Plan → Act → Observe → Refine. The model makes a move, sees what happened, then decides what to do next. However, you should set limits on steps, costs, and time to avoid runaway loops.
• Human in the loop (HITL). Not every task should run without oversight. Modern frameworks support breakpoints where a human can review an agent’s action before it continues. For example, if your agent’s about to initiate a bank transfer or delete user data, someone should sign off first.
• State and memory management. Agents handling complex tasks often need memory — the ability to retain information across steps. Make sure your agent framework supports state persistence. Some provide built-in checkpoints and persistence features to store intermediate results outside the prompt. For longer sessions, consider saving conversation history or conclusions to an external datastore to avoid context window issues.
• Frameworks vs. low-code tools. “Low-code” agent builders like n8n are great for getting started quickly without much programming. But as your workflows grow, you’ll probably hit a ceiling. Code-first frameworks like LangGraph give you more control, flexibility, and observability when it matters. A good strategy is to prototype with low-code and transition to code when needed.
• Agent vs. workflow. Don’t build agents just because it sounds smart. Many tasks are better handled with a simple chain or script. Reserve agents for when you need variability or the sequence can’t be predetermined easily. For example, a lot of “smart” filing or form-filling tasks can be done with one-shot LLM calls or straightforward logic.
  
  

Example use cases:

• AI growth hacker (No-code tutorial: OpenAI Image API + Gumloop = 4 Facebook Ads in ONE Click)
  A no-code walkthrough showing how to set up a Gumloop agent that generates multiple Facebook ad variations using the OpenAI Image API, tracks their performance, and iterates on top-performing variants — all in one click. 
• Code refactoring agent (Tutorial: Google ADK Sequential Agents)
  A hands-on guide showing how to use Google’s Agent Development Kit to build a three-stage refactoring pipeline:
  
  • Code writer generates initial code from specs.
  • Code reviewer examines the code for errors and style issues.
  • Code refactorer applies reviewer feedback to improve performance and structure, including sample Python snippets for instantiating SequentialAgent, injecting state keys, and integrating Gemini LLM calls for each step.
• Travel planning assistant (Video tutorial: AI-Powered Trip Planner using CrewAI, LangChain & Streamlit)
  A multi-step agent that searches for flights, books hotels, and builds full itineraries, adapting its steps based on the traveler’s input. Built using CrewAI for planning logic, LangChain for orchestration, and Streamlit for the UI. 
• Exploratory data analysis agent (Video tutorial: Build An AI Agent For Data Analysis & Reporting With n8n)
  A step-by-step YouTube tutorial showing how to create an n8n workflow that ingests CSV or database data, uses code and AI nodes to perform exploratory data analysis (EDA), and then generates a concise summary report via an LLM—all fully automated within n8n’s visual editor.
  
  

Notable tools/platforms:

• LangChain and LangGraph. Agent frameworks with predefined agent types (e.g., ReAct), support for human-in-the-loop interrupts, memory management, and a graph-based API in LangGraph for visualizing and orchestrating tool calls.
• Lindy AI, Flowise, and LangFlow. Low-code or no-code interfaces for building LLM workflows, defining tools, and prototyping agent logic without heavy programming.
• OpenAI function calling. Enables plan → execute loops directly within GPT calls. No external framework required — you can just define JSON schemas and let the model decide which function to call.
• Auto-GPT, BabyAGI, and SuperAGI. Experimental open-source agents that break tasks into subtasks, manage loops, and invoke tools. They’re still evolving but useful for testing full-autonomy boundaries.
• LangSmith and APM tools like Datadog APM / Sentry. Tools for observability that help you trace what your agents are doing, where they fail, and how they perform in production. You can also use evaluation frameworks (OpenAI Evals) to systematically test agent behavior.
• nexos.ai  An enterprise AI gateway that routes traffic to 200+ LLMs via one API, with live cost tracking, fallbacks, guardrails, and governance. Great for enterprise-scale agent systems.
  
  

Recommended resources:

• Building Effective AI Agents by Anthropic (December 2024)
  A practical guide on planning loops, safety mechanisms, tool schemas, and real-world deployments.
• New tools for building agents by OpenAI (March 2025)
  Announcement and tutorial for OpenAI’s Agents SDK and Responses API with built-in tools like web/file search and local system control.
• What is an AI agent? by Harrison Chase (Jun 28, 2024)LangChain’s founder defines what agents are, what they aren’t, and how to build them properly.
• ReAct: Synergizing Reasoning and Acting in Language Models (ICLR 2023)Introduces the ReAct paradigm for interleaving chain-of-thought reasoning with external actions (e.g., API calls), improving interpretability, factuality, and task success across QA and interactive benchmarks.
• AutoGPT Challenges (GitHub wiki)A library of test cases and tasks for improving agent design, memory handling, and autonomy.

7. Evaluations and Observability (Testing and Monitoring AI Systems)

If you want to move beyond toy demos and build reliable, production-grade AI applications, you need two things: evaluations and observability. Evaluations are structured tests that check if your model behaves as expected — like unit tests for AI. Observability means tracking what your system is actually doing in the wild: logging, metrics, error monitoring, and more. Together, these let you catch problems early, improve performance, and scale with confidence.

By 2025, a mature ecosystem has emerged, from OpenAI’s eval harnesses and LangSmith dashboards to custom CI-integrated tests. The goal is to stop treating models like black boxes and instead inspect, test, and debug them like any critical software system.

‍

Key practices:

• Define success upfront. What does “good” look like in your application? That could be factual accuracy, fidelity to format, or user satisfaction. Clear criteria drive meaningful evaluation. For example, the criteria for a summarization app may include relevance and conciseness.
• Automate evaluations. Build a test suite of sample prompts and expected outputs or at least outcome criteria. For structured tasks like classification or extraction, it’s straightforward: compare against known labels. For generative outputs, you’ll need to get creative. Use reference answers, proxy metrics like BLEU scores, or bring in another model to act as a judge. A practical trick: ask an LLM to evaluate another model’s output (e.g., “Is this summary relevant? Yes or no”). This “LLM-as-a-judge” approach isn’t perfect, but it can scale well when human review isn’t feasible.
• Prevent regressions. Every prompt tweak, model update, or API switch can break something. For instance, maybe a prompt tweak made format accuracy drop – you’d catch that if you had tests for “output is valid JSON”. Integrating continuous evaluation into your dev workflow keeps you ahead of surprises. OpenAI’s own evaluation framework encourages community-contributed evals to probe model weaknesses.
• Monitor in production. Log inputs and outputs of the model (with user data considerations/anonymization as needed), timestamps and latency, tool calls, model versions, and errors (e.g., if the model failed to follow instructions, or if your code had to correct something). When something fails, you’ll want to know what the model saw and did.
• Track the right metrics. Beyond testing, track metrics that actually tell you how things are working in production. That includes success rates (e.g., how often does the model deliver a helpful answer?), token usage and cost, and latency (P50 and P95) to monitor user experience. Set alerts if latency spikes or costs creep up. Watch for model drift too — if performance degrades over time, you want to catch it early.
• Use user feedback. In many applications, user feedback can be a goldmine for evals. Explicit ratings, corrections, follow-up questions — all of these signal whether your system met the mark. If a query had to be escalated to a human, log that as a failure and learn from it.
  
  

Example use cases:

• Chatbot QA (Evaluate a Chatbot – Get started with LangSmith)
  Write tests against known queries. Track facts, tone, and policy alignment. Use LangSmith to log production chats, discover repeated failures (e.g., “refund policy for electronics”), and turn them into test cases.
• Financial report generation (finRAG Dataset: Deep Dive into Financial Report Analysis with LLMs)
  Extract key figures from filings, validate each number against source data, and score both the main answer and metadata like time period and currency to avoid hallucinations.
• Harm detection and filtering (Giskard LLM Scan Quickstart – Automatically Detect Harmful & Hallucinatory Outputs)
  Use Giskard’s LLM Scan to write “red flag” test cases (e.g., prompts that solicit disallowed content) and scan for harmful outputs. Automate fail/pass logging and keep a backlog of edge cases for future testing.
• Regression testing workflows (LLM regression testing workflow step by step)
  Treat LLM failures like software bugs: create “before” and “after” datasets, automate evaluation with the Evidently Python SDK, and build a dashboard to monitor test outcomes. Add each new real-world error case to your eval store so you never regress on critical scenarios.
  
  

Notable tools/platforms:

• OpenAI Evals. An open-source framework for defining and running LLM evaluations using Python code and YAML configs, with a registry of community-submitted benchmarks for common failures. 
• LangSmith. LangChain’s observability and evaluation platform: trace every LLM call and agent action, visualize token counts, latencies, and errors in customizable dashboards, and run evals directly in the UI.
• Gentrace. A monitoring tool that automates evaluations, tracks prompt versions, and sends regression alerts.
• Arize AI. LLM-specific observability with task-level metrics, drift detection, clustering of embedding performance, and integration with GPT for deeper analysis.
• WhyLabs. Privacy-safe monitoring and security observability for LLMs: flagging risks, tracking drift, and automating remediation across generative and predictive models. 
• Autoblocks. Links LLM testing and evaluation to business outcomes: configure online evaluators on live AI events to catch failures in production, enforce compliance checks, and reduce error rates. 
• Custom logging and APM. Combine your LLM stack with Elastic for log aggregation and an APM solution like Datadog or Splunk for performance metrics and real-time log analysis.
  
  

Recommended resources:

• What Is LLM Observability and Monitoring? (The New Stack, Mar 12 2025)
  A practical overview of why observability and monitoring are essential for catching quality, safety, and performance issues before they reach your users.
• LLM Observability: The 5 Key Pillars for Monitoring Large Language Models (Arize AI blog co-authored with Aparna Dhinakaran, Jan 1, 2024)
  A breakdown of the five pillars every team should cover: evaluation, traces and spans, RAG, fine-tuning, and prompt engineering — plus hands-on guidance for how to track each.
• A Field Guide to Rapidly Improving AI Products by Hamel Husain (March 24, 2025)
  A deep-dive playbook emphasizing error analysis, data-centric evaluation, and running fast experiments to improve AI features that matter. 
• What Is LLMOps? Key Components & Differences to MLOps (lakeFS Blog, 2025)
  Defines how LLMOps works in practice, covering version control for data and prompts, drift detection, CI/CD for prompt updates, and how it all differs from classic MLOps.
• Building Production-Ready LLM Applications: An Evaluation-First Approach by Manish Katyan (LinkedIn, 2025)
  A case study that shows how systematic evaluations, paired with good observability, directly improve business outcomes.
• AgentOps: Enabling Observability of LLM Agents (ArXiv, November 8, 2024)
  A technical taxonomy for AgentOps, detailing what to trace across perception, planning, actions, and feedback to keep your agents safe and debuggable.‍
• A Practical Guide to Integrate Evaluation and Observability into LLM Apps (Daily Dose of Data Science, January 18, 2025) A tutorial on using Opik (an open-source eval platform) for automated test suites, real-time tracing, and continuous regression in your LLM pipelines.

8. Model Context Protocols (MCPs) – Connecting Data and Persisting State

By late 2024, a new standard began reshaping how AI systems interact with external tools: Model Context Protocols (MCPs). Think of MCPs as the “USB-C” of AI integration — a shared protocol that allows any model to plug into any data source or service, as long as both sides speak the same language.

Traditionally, every AI app had to build its own patchwork of custom integrations. MCP changes that. Pioneered by Anthropic, the open-source MCP standard defines how an AI system (the client) connects with external “context servers” that provide live data or actions. This enables rich, persistent context across workflows, better continuity between interactions, and more grounded, real-time decision-making.

With MCP, the AI is no longer isolated from your databases or knowledge base. It can access them in a secure, structured way and remember context as it switches between them.

‍

Key concepts:

• Standardized context interface. MCP introduces a simple client-server model. An AI agent (MCP client) connects to lightweight MCP servers that expose specific capabilities, like reading from a CRM, querying a database, or accessing a shared drive. Because the protocol is standardized, the AI doesn’t need custom code for every integration. It just knows how to talk to MCP to get what it needs.
• Two-way interaction (rather than one-time prompts). Instead of stuffing all the data into a single prompt, the model can ask for what it needs on demand. For example, “get user’s recent files from Drive” or “update this record in Salesforce.” The MCP server returns the result, which becomes part of the model’s active context. The interaction is live, dynamic, and governed by the server’s permissions.
• Stateful, multi-turn reasoning. MCP enables agents to keep track of where they are in a workflow, even across tools. A coding assistant may pull from GitHub, query a documentation index, and post progress to Slack — all in a single session. Because each tool connection follows the same protocol, the agent can juggle them without dropping context. This persistent context across tools solves a long-standing issue with memory in LLMs.
• Grounding in private data. For enterprises, MCP offers a big win: grounding AI in real-time internal data, without leaking sensitive information. An MCP server can sit behind a company firewall, providing live access to current sales figures, inventory levels, or help desk logs without having to feed that data into the model’s prompt or retrain anything. 
• A growing open system. MCP is open-source and designed to be shared and extended. Early adopters like Block, Replit, Zed, and Sourcegraph are already building MCP servers. The goal is to create a plug-and-play ecosystem where developers can integrate with tools like Jira, Salesforce, or Gmail using prebuilt MCP connectors. This standardization could massively speed up the development of AI-powered applications.
  
  

Example use cases:

• Enterprise assistant (How to Setup & Use Jira MCP Server)A step-by-step guide to setting up a Jira MCP server. You’ll configure OAuth, install the MCP-Atlassian Python package, and connect an AI like Claude to create, search, and update Jira issues or Confluence pages directly from a chat interface.
• Coding partner (Use MCP Servers in VS Code (Preview)VS Code’s official preview shows how to enable agent mode, register MCP servers, and let Claude or GitHub Copilot handle code lookups, Git commands, and workspace edits with natural-language instructions.
• Personal AI butler (From Natural Language to Real Actions: The Magic of MCP Servers)This hands-on Medium guide shows how to wire up multiple MCP servers into one assistant. The result is a personal AI that can check your inbox, create tickets, summarize docs, and coordinate actions across tools.
• Research assistant (True Agentic RAG: How I Taught Claude to Talk to My PDFs using Model Context Protocol (MCP))A tutorial on building a file-server for PDFs using MCP. It walks through document cataloging, chunking content, and guiding Claude through a structured retrieval and summarization workflow. It’s great for deep research that spans large files or multiple documents.
  
  

Notable tools/platforms:

• Sourcegraph Cody and Codeium. IDE copilots that tap into MCP for broader context: fetching code definitions, searching docs, and running Git commands inside VS Code, JetBrains, and other popular editors.
• LangChain MCP adapters. Official adapters and utilities that let LangChain agents use MCP tools (e.g., file servers or code servers) just like any other function.
  
  

Recommended resources:

• Anthropic – “Introducing the Model Context Protocol” (Nov 25, 2024)
  The official announcement explaining why MCP exists, how it works, and how it enables safer, more capable AI assistants.
• Anthropic documentation – Tool Use (MCP).
  Explains how Claude (including the Desktop app) integrates with MCP servers for file access, code editing, and custom tool use. Includes SDK examples and setup instructions.
• MCP security and governance guide. Guidance on running an MCP gateway with audit logs, access policies, and data masking to enforce enterprise data governance when AI agents query sensitive internal systems.
• Amanatullah’s Medium deep dive on MCP (Mar 2025)
  A technical breakdown of how MCP handles messaging, tool registration, and real-world integration with custom tools.
• r/LocalLLaMA on Reddit
  A developer community sharing open-source MCP tools, local LLM workflows, and setup guides for getting started.
  

• #AIEngineering on Twitter
  ‍A live feed of demos, insights, and developer threads on AI engineering topics, including a growing list of MCP examples in action.

9. AI Code Editors (Accelerating Development with AI Pair Programmers)

AI code editors and assistants have fundamentally changed how developers work. Tools like GitHub Copilot, Cursor, Codeium, and Windsurf now assist with everything from writing functions based on comments to suggesting entire refactors. By 2025, they’ve gone from novelty to standard toolkit embedded in IDEs or built into standalone editors like Cursor. Their core value: handling repetitive tasks, accelerating boilerplate, offering relevant code suggestions, and freeing up engineers to focus on logic, architecture, and quality. These tools won’t replace developers, but they do shift what developers spend time on.

Key concepts:

• Productivity gains. The numbers are clear: AI assistants can cut development time by a double-digit percentage. Microsoft’s early studies showed strong gains in task speed, and more recent research points to a ~26% improvement in throughput. That means what used to take five days might now take four, especially when the assistant kicks off your work with structured stubs or eliminates time wasted searching for syntax or examples.
• Best uses for AI editors. These tools are particularly good at:
  
  • Generating boilerplate (e.g., parsing JSON, writing CRUD methods)
  • Translating code between languages
  • Writing and updating unit tests
  • Suggesting bug fixes and improvements
  • Exploring APIs or libraries with in-context examples
  • Advanced features like Cursor’s Copilot++ predict multi-line edits and whole-file refactors
  • Many tools also come with a chat interface, making it easy to get help
• Workflow integration. AI editors are built into VS Code, JetBrains, and other popular environments. A few typical ways of using them look like this:
  
  • Write a comment → get the function written
  • Highlight a function → ask for improvements
  • Pose a question in the side panel chat

The most effective use is iterative: accept good suggestions, tweak what’s off, and refine your inputs. Essentially, coding becomes more interactive and conversational.

• Quality and oversight. These tools can be fast, but they’re not flawless. Bugs, logic errors, and security issues still happen. One study found around 27% of code suggestions had security flaws (Majdinasab et al., 2024). The rule of thumb is to treat AI code like you’d treat work from a junior teammate: review, test, and improve. There’s also the matter of license compliance – early versions of Copilot sometimes suggested code verbatim from training data. Tools have improved at filtering out such leaks or copyright concerns, but diligence still counts.
• Shifting skill sets. The value of memorizing syntax is fading. Instead, developers are focusing more on architecture, debugging, and system-level thinking. Junior devs can ship working code faster, but senior devs are still essential for designing robust solutions and overseeing quality. “Prompt engineering for code” is now a real skill — knowing how to phrase requests to get the right output.
• Faster prototyping. One major shift: solo developers and tiny teams can now ship polished MVPs at startup speed. We’ve seen solo founders launch products and hit $1M ARR with no full-time engineers. With AI handling much of the coding grunt work, building and iterating on ideas is faster than ever, which ties directly into Principle 10 (Build a First Version Fast).
  
  

Example use cases:

• GitHub Copilot in VS Code (How To Use GitHub Copilot: Comments)
  A quick-start guide to using Copilot: how to write a comment, trigger completions, and turn rough ideas into working code without breaking your flow.
• Cursor AI editor (Cursor AI: A Guide With 10 Practical Examples)
  Covers installing Cursor and using it to optimize real code until the performance hits the target.
• CodiumAI in JetBrains IDE (How To Generate Tests Using CodiumAI)
  A hands-on walkthrough: install the CodiumAI plugin, write a function, and instantly get a suite of suggested unit tests.
• Windsurf AI Editor (Windsurf AI Agentic Code Editor: Features, Setup, and Use Cases)
  Set up Windsurf and use features like Cascade or Supercomplete to rewrite and refactor code blocks. The tool applies structured improvements and shows you the changes with context.
  
  

Notable tools/platforms:

• GitHub Copilot: The original, still evolving. Best-in-class completions and ecosystem support.
• Cursor: An AI-native code editor with built-in debugging and refactoring help.
• Codeium: Fast, free autocomplete and inline code suggestions across major IDEs.
• Replit Ghostwriter: Real-time pair programming for browser-based dev environments.
• Windsurf AI. An experimental agentic editor focused on large-scale refactors and context-aware edits.
  
  

Recommended resources:

• 15 Best AI Coding Assistant Tools in 2025 – Qodo
  Breaks down the top AI coding assistants by strengths, IDE support, and pricing.
• The Impact of AI on Developer Productivity: Evidence from GitHub Copilot (arXiv)
  Empirical research showing a 55.8% speed increase on real development tasks when using Copilot. Proof that the productivity gains are more than hype.
• Develop a Mobile App in One Day using GitHub Copilot
  A practical blog guide that walks through building a working mobile app in under 24 hours using Copilot.‍
• Windsurf vs Cursor: which is the better AI code editor? – Builder.io
  Side-by-side comparison of two leading AI code editors. Reviews context handling, feature depth, UI design, and pricing to help you pick the right tool for your stack.

10. MVP Thinking (Minimum Viable Product and Business Mindset)

With powerful AI tools at your disposal, it’s easy to overbuild or lose focus. MVP thinking is your guardrail. It’s about stripping your idea to its most valuable core, shipping the minimum viable product (the simplest version of your idea that can work), and building a minimum viable business around it. In a field that evolves weekly, speed matters. Build small, get feedback fast, and learn what actually works before scaling.

This isn’t just a technical approach — it’s a mindset. What matters is solving a real problem, delivering real value, and validating that someone wants what you're building. Not every task needs AI, and perfection is a trap. The goal is to do one thing well enough that people care.

‍

Key practices:

• Focus on the core value. What’s the one thing your product should do exceptionally well? That’s your MVP. Nail that before layering on UI polish or extra features. Don’t build dashboards if the engine doesn’t run. If your product summarizes dense documents into key takeaways, make sure it does that better than anything else. As one guide noted, a modern MVP should be focused, intentional, and demonstrate value immediately – it can be lean but must feel reliable in doing its one job.
• Don’t automate everything (at first). AI is powerful but not always necessary — you need to choose the right automation targets carefully. For early versions, manually handling edge cases or using simpler logic can save weeks of work. For example, if you're building a support agent, start by answering FAQs and hand off the rest to humans. Solve the easy 80% now, and the final 20% can come later, once you’ve proven there’s a need.
• Handle edge cases gracefully. You won’t anticipate all edge cases, but the minimum product still needs to be reliable. Users don’t forgive bugs just because it’s an MVP. Plan for things going wrong. If the AI’s unsure, have it say so. If it doesn’t know the answer, log the query and move on. Transparency beats guesswork, and robust fallbacks build trust, even in a lean product.
• Use failure to your advantage. Your MVP is a learning tool — its purpose is to test assumptions and reveal what needs to be fixed. If something breaks or users complain, good. That’s feedback. Learn from it, iterate, and ship again. You’ll move faster than teams trying to get everything “right” before launch.
• Stay grounded in feasibility. If your AI idea is bleeding-edge, don’t bet the whole product on it working out of the gate. Use a lighter version or even a “Wizard of Oz” approach (with a human-in-the-loop behind the scenes) to test demand. Validate the concept first. Once you know people want it and exactly what they want, then optimize and automate.
• Don’t forget the business. An MVP is also about validating the business case. Talk to users. Will they pay for this? Does it save them time? Can you scale it sustainably? Keep an eye on metrics like user retention or conversion even at MVP stage. If you can’t show traction or find real users early, rethink your direction. Don’t spend months building something people didn’t ask for.
  
  

Examples of YC startups that nailed MVP thinking:

• HumanLayer (YC F24)
  MVP: A lightweight Python/TypeScript SDK and Slack integration to inject human approvals before critical agent actions, like dropping an unused SQL table. Shipped with just one approval flow to prove the concept of human-in-the-loop safety.
• finbar (YC W25)MVP: A basic Python pipeline and UI that turns messy financial PDFs into clean time-series data. Deployed to a single hedge fund, cut hours of grunt work, and landed early revenue without overbuilding.
• co.dev (YC W23)MVP: A chat interface that turns a user prompt into a working Next.js + Supabase CRUD app. Users could instantly download and own the code. 
• Stamp (YC W25)
  MVP: A Chrome extension that drafts email replies, extracts action items, and filters inbox noise. Released to a small beta group to test time saved and real-world usability before going further.

‍

Notable tools/platforms:

• Bubble: A visual web builder that lets you design, host, and launch interactive web apps without writing code.
• Streamlit: A Python framework for quickly turning data scripts into shareable web apps with simple commands.
• Dash: A Python framework for building analytical web applications with interactive charts and dashboards.
• PromptFlow: An OpenAI tool for developing, testing, and deploying complex prompt workflows with version control and observability.
• Promptfoo: A prompt management and testing library that lets you run prompt tests at scale and track performance metrics.
• Typeform: A conversational form builder that makes collecting user input feel human to boost user engagement and response rates.
• Slack: A team collaboration tool where you can gather real-time feedback, run beta communities, and integrate chat-based surveys.
• PostHog: An open-source product analytics suite for tracking user behavior, feature usage, and funnel analysis.
• Google Analytics: A free web analytics service that provides insights into website traffic, user behavior, and conversion metrics.
• Lean Canvas: A strategic business planning tool for mapping key aspects of a startup’s model on a single-page canvas.
• Optimizely: A digital experience platform specializing in A/B testing and experimentation across web and mobile channels.
  
  

Recommended resources:

• The Lean Startup by Eric Ries
  Still the bible for MVP builders. Build fast, test often, don’t waste time. 
• AI MVP Development: A Basic Guide (UpsilonIT, April 17 2025)
  Step-by-step advice on planning, tool selection, and iterative testing to ship a working AI MVP quickly.
• Effective Product Management: Building Successful Generative AI-Powered MVPs
  Covers the realities of working with LLMs in v1 products, from choosing pre-trained APIs to handling unpredictability in outputs. 
• How Jasper Found Product-Market Fit: Pivoting to AI-Native SaaS by Unusual VC
  Inside look at how Jasper focused hard on one feature, proved value, and scaled.
• Minimum Viable Quality Is The Heralded Rising Star For AI Startups and Venture Capital Funding (Forbes, Aug 20 2024)
  Explains why the minimum viable quality is the critical metric for early-stage AI products.‍
• Y Combinator – “How to Build a Gen AI MVP” (YouTube)
  A practical video walkthrough on stripping ideas down to value and validating them quickly with users.

Final note

These 10 principles are a practical blueprint for building AI products that actually work in 2025. From choosing the right models to designing effective prompts, integrating the right tools, testing thoroughly, and shipping with purpose, this guide is about turning ambition into execution.

AI engineering is a multidisciplinary craft. But if you stick to these patterns, you’ll move faster, avoid the usual traps, and build tools people trust and use. Keep it lean. Stay grounded. Good luck, and happy building!