10 AI Engineering Principles in 2026: The Complete Beginner's Guide to Building Real AI Products

2. Context and Retrieval: How to Feed Your Model the Right Information

Models are brilliant but forgetful. They only know what you show them. Retrieval-Augmented Generation (RAG) is the technique of finding the right information and handing it to the model at the moment it needs it — like a research assistant who pulls the relevant file before you ask your question.

Why this matters to you

Without RAG, your model can only work with its training data (which has a cutoff date) and whatever you paste into the prompt. That's fine for casual questions. It falls apart the moment you need the model to answer questions about your data — your company's documents, your product catalog, your customer records.

RAG bridges this gap. It's the most commonly deployed AI engineering pattern in production, and it's the first real skill that separates "I played with ChatGPT" from "I built an AI product."

How RAG works (the simple version)

Imagine you're building a customer support bot for a shoe company. The model doesn't know your return policy. Here's what RAG does:

1. Prepare: Take all your support documents and convert them into a searchable format. Each document gets sliced into chunks (paragraphs or sections), and each chunk gets converted into a mathematical representation called an "embedding" — a list of numbers that captures the meaning of the text. Store these in a vector database.

2. Retrieve: When a customer asks "Can I return shoes after 60 days?", the system converts this question into an embedding too, then searches the vector database for chunks whose meaning is closest to the question. It finds the chunk containing your return policy.

3. Generate: The system pastes that chunk into the prompt alongside the customer's question. The model reads both and answers: "Our return window is 30 days, so unfortunately..."

The model never memorized your return policy. It read it just-in-time, like a human scanning a document.

RAG vs. just stuffing everything in the prompt

"Wait," you might ask. "Why not just paste all my documents into the prompt? Models have huge context windows now."

Good instinct. Here's why RAG still wins:

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The cost gap is roughly 1,250x. For a prototype with 10 documents, long context works fine. For a production system handling 10,000 queries a day across 50,000 documents, RAG is the only viable path.

The biggest mistake beginners make with RAG

Chunking badly. When you slice your documents into pieces, the quality of those pieces determines everything downstream. A chunk that splits a sentence in half, or separates a heading from its content, poisons the results.

A CDC study found 80% of RAG failures trace back to chunking decisions — not to the model, not to the database, not to the prompt. The fix: chunk by natural boundaries (paragraphs, sections, headers). Use fewer, longer chunks (5–10 per query) rather than many tiny ones. And test your chunks by asking: "If I handed this chunk to a human with no other context, could they understand it?"

Tools to get started

- Vector databases: Pinecone (managed, easy), Chroma (lightweight, local), Weaviate (open-source, flexible)

- Frameworks: LangChain or LlamaIndex — both give you pre-built RAG pipelines you can set up in under an hour

- Embeddings: OpenAI's `text-embedding-3-large`, or open-source `Qwen3-Embedding-8B` (free, state-of-the-art)

Try it today

Build a simple RAG system over your own documents in an afternoon using LangChain's quickstart tutorial. You'll learn the full loop: load docs → chunk → embed → store → retrieve → generate.

3. Context Engineering: The New Core Skill (Formerly "Prompt Engineering")

The art of giving an AI model everything it needs to succeed on a task — the right instructions, the right examples, the right data, in the right format. This used to be called "prompt engineering." In June 2025, the field outgrew that name. Crafting a single prompt is now a tiny fraction of the work.

Why the name changed (and why it matters to you)

When models were simpler, the skill was writing a clever prompt — a few sentences that tricked the model into performing well. That still matters, but modern AI systems are more complex. A production AI application might feed the model:

• A system instruction (who to be, how to behave)
• The user's question
• Retrieved documents from a RAG pipeline
• Results from previous tool calls
• Conversation history
• Agent state (what step the system is on)
• Examples of desired behavior

All of this is “context.” Prompt engineering is one piece. Context engineering is the whole puzzle. Shopify's CEO coined the term on June 18, 2025: “Context engineering is the art of providing all the context for the task to be plausibly solvable by the LLM.” Within a week, Andrej Karpathy endorsed it. By September, Anthropic formalized it into their documentation.

The fundamentals still apply

Even though the name changed, the underlying skills are the same — they just expanded in scope:

Be specific. “Summarize this” is worse than “Summarize this email in 2 sentences, focusing on action items, in a professional tone.” The model does what you say, not what you mean. Treat it like a smart but literal colleague.

Show, don’t just tell. A few examples of input → desired output (called few-shot examples) work better than paragraphs of instructions. If you want the model to extract dates from text, show it two examples of text with dates extracted. It’ll pattern-match.

Structure your inputs. Use clear sections: Context:, Task:, Output Format:. Models respond well to structure — it reduces ambiguity and constrains the output. Ask for JSON when you need data. Ask for bullet points when you need lists.

Ask for structured output. Both OpenAI and Anthropic now support structured outputs — a feature where the model is guaranteed to return valid JSON matching a schema you define. This eliminates an entire class of bugs where the model’s response is well-written but unparseable by your code.

What changed for reasoning models

Here’s a nuance that trips people up: with modern reasoning models, chain-of-thought prompting (“let’s think step by step”) offers diminishing returns. These models already reason internally. A Wharton study found that adding chain-of-thought prompts to reasoning models produced only marginal accuracy gains while increasing response times by 20–80%. Save the technique for older or smaller models.

Try it today

Take a task you’ve been doing with ChatGPT. Rewrite your prompt using this template:

Role: You are a [specific role]
Context: [Background information the model needs]
Task: [Exactly what you want done]
Constraints: [Rules, limitations, things to avoid]
Output Format: [How the response should be structured]
Examples: [1-2 input/output examples]

Compare the output to your original prompt. The difference is usually dramatic.

4. Tools and MCP: Teaching Your Model to Do Things

Models don't just talk — they act. Tool use is how you give an AI model the ability to call APIs, query databases, run calculations, search the web, send emails, or interact with any external service. Without tools, a model is a brain in a jar. With tools, it becomes a capable assistant.

Why this matters to you

The gap between "cool demo" and "useful product" is almost always tools. A chatbot that can only talk about your inventory is a novelty. A chatbot that can check your inventory, process a return, and send a confirmation email — that's a product.

How tool use works

You define a set of functions (tools) the model can call, each with a name, description, and expected parameters. When the model decides it needs to use a tool, it outputs a structured request instead of text. Your code catches that request, executes the function, and feeds the result back to the model. The model then uses the result to continue its response.

Example flow:

1. User: "What's the weather in Berlin?"

2. Model thinks: "I should call the weather tool"

3. Model outputs: `{"tool": "get_weather", "params": {"city": "Berlin"}}`

4. Your code calls the weather API → gets "8°C, cloudy"

5. Model receives the result and responds: "It's 8 degrees and cloudy in Berlin right now."

The model never accessed the internet. Your code did. The model just decided when to use the tool and how to interpret the result.

MCP: The universal plug for AI tools

Here's where 2025-2026 delivered something genuinely new. Model Context Protocol (MCP) is a standard that Anthropic created and the entire industry adopted. Think of it as USB-C for AI: one universal plug that connects any model to any tool.

Before MCP, every model-to-tool connection required custom code. Want Claude to read from your database? Build a custom integration. Want GPT to do the same? Build another. Want to switch models? Rebuild everything.

MCP eliminates this. You set up an MCP server once for each tool (database, Slack, GitHub, etc.), and any MCP-compatible model can use it. The numbers speak to adoption: 97 million monthly SDK downloads and over 10,000 active public MCP servers by February 2026.

On December 2025, Anthropic donated MCP to the Linux Foundation, co-governed by Anthropic, OpenAI, Google, Microsoft, and AWS. This isn't a proprietary standard. It's industry infrastructure.

Why you should care as a beginner

MCP means you can give your AI application superpowers without building custom integrations. Want your AI assistant to access Jira, Google Drive, Slack, and your database? There are pre-built MCP servers for all of them. You configure, not code.

Tools to get started

- OpenAI's function calling — guide here. The simplest way to add a tool to any GPT-powered app.

- Anthropic's tool use — documentation here. Claude's equivalent, with MCP built in.

- Pre-built MCP servers — Browse mcp.so for 17,000+ community servers covering everything from GitHub to Stripe to databases.

Try it today

If you use Claude Desktop, you can add an MCP server in minutes. The filesystem MCP server lets Claude read and write files on your computer. Install it, restart Claude, and ask it to "read the contents of my Downloads folder." You just gave an AI model a new ability.

5. Fine-Tuning: When to Customize a Model (And When Not To)

Fine-tuning takes a general-purpose model and trains it on your specific data to make it better at your specific task. Think of it as the difference between a general contractor and a specialist — the specialist knows your domain's quirks without being told every time.

Why this matters to you

Most beginners jump to fine-tuning too early. It's powerful, but it's also expensive, time-consuming, and easy to screw up. The rule of thumb every experienced AI engineer follows:

1. First, try a better prompt. Free. Takes minutes.

2. Then, try RAG. Feed the model better data. Takes hours.

3. Only then, fine-tune. Retrain the model on your examples. Takes days and money.

Fine-tuning makes sense when you have a pattern that's stable, repeated thousands of times, and too complex to describe in a prompt. Examples: matching your brand's writing voice across every output, handling industry-specific jargon the model misunderstands, or consistently formatting outputs in a way prompts can't achieve.

What's new in 2026

Three flavors of fine-tuning are now available through OpenAI's API:

- SFT (Supervised Fine-Tuning): Show the model examples of desired input/output pairs. The classic approach. Good for format, tone, and domain adaptation.

- DPO (Direct Preference Optimization): Show the model two outputs and tell it which one you prefer. Good for subjective quality — "this response sounds more professional than that one."

- RFT (Reinforcement Fine-Tuning): Give the model a grading function and let it learn to maximize the score. Good for tasks with clear right/wrong answers — math, code, compliance checks.

Lightweight techniques mean you don't need to retrain the whole model. LoRA (Low-Rank Adaptation) and its successor QDoRA let you train a small "adapter" that sits on top of the base model. You update millions of parameters instead of billions. It's faster, cheaper, and you keep the model's general intelligence intact.

The honest truth for beginners

You probably won't fine-tune anything in your first six months. And that's fine. The combination of good prompts + RAG + model selection handles 90% of use cases. Save fine-tuning for when you've exhausted the simpler options and have clear evidence that your model keeps failing in a specific, repeatable way.

6. Agents: When AI Systems Make Decisions

An AI agent is a system where the model decides what to do next, rather than following a fixed script. Give it a goal, give it tools, and it figures out the steps. A simple chatbot responds to questions. An agent can plan a sequence of actions, use tools, check the results, and adapt.

Why this matters to you

Agents are the most hyped topic in AI engineering. They're also the most misunderstood. Here's the honest picture.

What agents can do today (February 2026): Handle customer service queries by looking up orders, checking policies, and issuing refunds. Write code, run tests, and fix bugs across multiple files. Research topics by searching multiple sources and synthesizing results. Schedule meetings, send emails, and manage routine workflows.

What agents struggle with: Open-ended goals with ambiguous success criteria. Long, multi-step plans where one mistake cascades. Tasks requiring judgment calls that are hard to define programmatically. Anything where "almost right" creates real harm (financial transactions, medical decisions).

The Klarna cautionary tale

In 2025, Klarna (the fintech company) replaced 700 customer service agents with AI, claiming $60M in savings and handling two-thirds of all chats. It made headlines. Then the quality complaints rolled in. CEO Sebastian Siemiatkowski admitted the company had prioritized cost over quality. Klarna rehired human agents for complex queries, keeping AI only for routine ones.

The lesson: agents work best as specialists handling well-defined tasks, not as replacements for human judgment across the board. A LangChain survey found that 57% of companies have agents in production — but customer service (26.5%) and research (24.4%) dominate because those tasks have clear boundaries.

The decision: agent vs. workflow

Before building an agent, ask: does the right sequence of steps depend on the input?

- "When a customer emails, classify the issue, look up their account, and draft a response" → This is a workflow. The steps are always the same. Use a simple pipeline.

- "Help the user plan a trip based on their preferences" → This might need an agent. The steps vary (some users need flights, some don't; some want restaurant suggestions, others don't). The model needs to decide what to do based on each user's input.

Don't build an agent when a workflow will do. Agents are harder to test, debug, and predict.

Frameworks to know

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Try it today

Start with OpenAI's Agents SDK tutorial. Build a simple agent that takes a user question, decides whether to search the web or answer from knowledge, and responds. That's the core loop: plan → act → observe → respond.

The agent to watch in 2026: OpenClaw

Most agent frameworks give you building blocks and say "good luck." OpenClaw takes a different approach — it's an open-source agent designed to handle real-world tasks end-to-end, from browsing the web to executing multi-step workflows, with built-in guardrails that keep it from going off the rails. Where other agents need you to wire together tools, memory, and decision logic by hand, OpenClaw ships those pieces pre-assembled.

We recommend spending an afternoon with it. The gap between "I read about agents" and "I watched an agent book a flight, hit an error, recover, and confirm the booking" is the gap between theory and conviction. This walkthrough shows what that looks like in practice:

7. Evaluations: How to Know If Your AI Actually Works

Evaluations (evals) are tests for AI systems. They answer the question every stakeholder will ask you: "How do we know this is working?" Without evals, you're flying blind — shipping changes and hoping nothing breaks.

Why this matters to you

Here's what happens without evals: you tweak a prompt, the chatbot seems better in your quick test, you ship it. Three days later, customer complaints spike because the tweak broke a different scenario you didn't test. This is the most common failure pattern in AI engineering. Evals prevent it.

Think of evals the way a software engineer thinks about unit tests. You wouldn't ship code without tests. Don't ship AI without evals.

What to test

Accuracy: Does the model give correct answers? For factual tasks, compare against known answers. For a support bot: "When asked about our return policy, does the model cite the correct 30-day window?"

Format: Does the output match the expected structure? If your code expects JSON, test that the model always returns valid JSON. If you need bullet points, test for bullet points.

Safety: Does the model ever say something harmful, wrong, or off-brand? Create adversarial test cases — questions designed to trip the model up — and verify it handles them gracefully.

Consistency: Does the model give the same quality of answer when you ask the same question ten times? Models are probabilistic, so some variation is expected, but the answer should remain correct.

The "LLM-as-a-judge" trick

For tasks where "correct" is subjective (e.g., "Is this summary good?"), you can use a second model to evaluate the first. Ask GPT-5.2: "Rate the quality of this summary on a scale of 1-5, considering accuracy and conciseness." This approach reaches roughly 80% agreement with human evaluators — not perfect, but good enough to catch regressions at scale.

The known pitfall: models tend to prefer longer responses (~15% score inflation) and have position bias (the first option in a comparison gets a ~40% unfair advantage). Use randomized ordering and focus on relative comparisons rather than absolute scores.

The minimum eval setup

For your first AI project, create a spreadsheet with three columns:

1. Input — A test question or task

2. Expected output — What a correct response looks like

3. Pass/Fail criteria — How to judge (exact match? Contains key phrase? Valid JSON?)

Start with 20–30 test cases. Run them after every change. That's it. You're already ahead of most teams.

Tools to know

- Langfuse — Open-source, MIT-licensed. Best starting point for logging and evaluating LLM calls.

- LangSmith — LangChain's platform. Good integration if you're using LangChain.

- Promptfoo — CLI-based eval tool. Define tests in YAML, run them from the terminal. Perfect for CI/CD pipelines.

8. AI Code Editors: The 180-Degree Shift You Need to Understand

AI code editors started as autocomplete plugins inside your IDE. That era is over. By February 2026, the center of gravity has swung 180 degrees — from GUI-based editors with AI bolted on, to CLI-first coding agents where you describe what you want in plain English and the AI writes, tests, and commits the code. The interface flipped: instead of you writing code with AI suggestions, the AI writes code with your supervision.

Why this matters to you

If you're entering AI engineering, the tool you'll reach for first probably won't have a graphical interface at all. The two most prominent tools in this new paradigm — Claude Code and OpenAI Codex — both run in the terminal. You type a task description. The agent reads your codebase, plans the changes, writes the code, runs the tests, and presents a diff for your approval. The programmer's job shifted from typing code to reviewing code and defining intent.

This is the walkthrough that convinced our team the shift was real — watch it before you form an opinion:

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The old world vs. the new world

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The leaders

Claude Code emerged as the tool that turned heads. It's a command-line agent — you point it at a repository, describe what you want ("add user authentication with OAuth, write tests, update the README"), and it executes across multiple files without hand-holding. Anthropic projects $500M+ annualized revenue from Claude Code alone. The developers who adopted it early report that it changed not just their speed but their workflow — they spend mornings defining tasks and afternoons reviewing output.

OpenAI Codex takes the same principle into the cloud. It runs asynchronously: submit a task, go do something else, come back to a completed pull request. For teams running multiple projects, the ability to queue up coding tasks and batch-review results collapsed timelines that used to stretch across sprints.

Cursor still matters — it reached $500M+ in annual revenue and a $9.9B valuation — but its 2.0 release tells the story: the headline feature was Agent Mode (8 parallel AI agents working across files), not better autocomplete. Even the IDE-first camp followed the current toward agentic, CLI-style coding.

The productivity debate

Research gives mixed signals, and the honest picture matters:

- Faros AI (10,000+ developers): Teams using AI completed 21% more tasks and merged 98% more pull requests.

- MIT/Princeton/Microsoft: Copilot users completed 26% more tasks, with juniors seeing bigger gains (21–40%) than seniors (7–16%).

- METR (randomized trial, 16 experienced developers): AI users were 19% slower — despite believing they were 20% faster.

The takeaway: AI code editors help most when you're learning, writing boilerplate, or exploring unfamiliar codebases. They help least (and can hurt) when you're an expert doing deep, familiar work. In all cases, review AI-generated code the way you'd review code from a junior teammate — it's often right, sometimes subtly wrong.

Try it today

Install GitHub Copilot or download Cursor (free tier available). Write a comment describing a function you need, hit Tab, and watch. Then review what it wrote — that review process is where you learn fastest.

9. Evaluations in Production: Observability and Monitoring

Observability means watching what your AI system does in the real world — logging inputs and outputs, tracking costs, catching errors, and measuring whether users are getting good results. Evals test your system before you ship. Observability tests it while it's running.

Why this matters to you

Models behave differently in production than in testing. Users ask questions you didn't anticipate. Edge cases appear. Costs creep up. The model that aced your test suite stumbles on real queries. Without observability, you won't know until users complain — or leave.

What to track

- Latency: How long does each request take? Users notice after 2–3 seconds.

- Cost: How much are you spending per request? Per day? Is it trending up?

- Error rate: How often does the model return unusable responses?

- User feedback: Thumbs up/down, follow-up questions, escalations to a human — all signals.

The standard stack

OpenTelemetry became the official standard for AI observability on January 21, 2025, when the AI/LLM semantic conventions were adopted. If you're familiar with application monitoring (Datadog, New Relic, etc.), this is the same idea extended to AI. Every model call becomes a traceable "span" with metadata: model used, tokens consumed, latency, cost.

The practical tools:

- Langfuse: Open-source. Log every LLM call, track costs, manage prompts. The best free option.

- Datadog LLM Observability: Enterprise-grade. Connects LLM traces to your existing application monitoring.

- Braintrust: Evaluation + observability combined. Notion's AI team reported 10x faster development after adopting it.

The pattern: evaluation-gated deployment

The most mature AI teams now block deployments if quality metrics drop. The CI/CD pipeline runs eval tests, and if the pass rate falls below a threshold, the deploy stops. It's the same principle as blocking a release if unit tests fail — applied to AI. If you adopt this pattern early, you'll save yourself countless production fires.

10. MVP Thinking: Ship Something That Works

Build the simplest version of your AI product that solves one real problem, ship it, and learn from what happens. This principle isn't about AI specifically — it's about not wasting months building something nobody wants.

Why this matters to you

Here's an uncomfortable observation: scroll through LinkedIn, X, or any tech conference agenda in February 2026, and you'll find ten people talking about AI for every one person building with it. Threads dissecting model benchmarks get thousands of likes. Actual shipped products get crickets. The ratio of commentary to creation has never been more lopsided.

Don't be a commentator. Be a builder.

AI makes it tempting to overbuild. The technology is so capable that you'll see possibilities everywhere. "What if the agent also scheduled meetings AND wrote reports AND analyzed data?" Stop. Do one thing well. Prove people want it. Then expand.

The 2025 startup graveyard is full of AI companies that built impressive technology for problems nobody was willing to pay to solve. Builder.ai raised $445M, reached a $1.5B valuation, then filed for bankruptcy. Humane sold its assets for a fraction of the investment. The pattern: technology-first, customer-second.

Where to find problems worth solving

If you're stuck on "what should I build?", skip the brainstorming. Y Combinator publishes a living document of the specific problems they want startups to solve — and the February 2026 edition is packed with AI-native opportunities: AI-powered compliance, AI for defense, foundation models for niche domains, AI-driven manufacturing, and more.

👉 Y Combinator's Requests for Startups (RFS)

You don't need to start a funded company to use this list. Treat it as a cheat sheet for what the most experienced startup investors on the planet believe people will pay for. Pick one category. Build a weekend prototype. Ship it to five users. That single act puts you ahead of 90% of the people debating model architectures on social media.

The framework

Step 1: Pick one problem. Not "improve customer service." Try: "Answer the 5 most common return-policy questions accurately and instantly."

Step 2: Build the simplest possible solution. A RAG pipeline over your FAQ documents, connected to a chat interface. No custom agents. No fine-tuning. No complex orchestration.

Step 3: Ship it to real users. Even 10 users is enough. Watch how they use it. What questions does it fail on? What do they actually ask (versus what you assumed they'd ask)?

Step 4: Improve based on evidence, not assumptions. Turn failed queries into eval test cases. Add more documents to your RAG pipeline. Upgrade the model if needed.

Step 5: Only add complexity when you've earned it. Add tools when users need actions (not just answers). Add agents when the workflow requires dynamic decisions. Fine-tune when you've identified a consistent failure that simpler methods can't fix.

The tools that accelerate MVPs

- Streamlit: Turn a Python script into a web app in 30 lines of code. Perfect for internal tools and prototypes.

- Cursor or Claude Code: Build your first AI product faster by using AI to write the code. Meta, but effective.

- Vercel's v0: Generate frontend UI from descriptions. Ship a polished-looking prototype in hours.

The mindset shift

The old world rewarded specialization: spend years mastering one technology stack. The AI engineering world rewards breadth and speed: pick up tools quickly, build prototypes fast, validate assumptions with users, and iterate. You don't need to be an expert in everything. You need to be good enough at each principle in this guide to ship something real.

Frequently Asked Questions

Do I need a machine learning background to become an AI engineer?

No. Most AI engineers come from software development, data science, or even non-technical backgrounds. The job is about using models, not building them from scratch. You need solid programming skills (Python is the primary language), an understanding of APIs, and the willingness to learn the concepts in this guide. At Turing College, the majority of our AI engineering graduates started as traditional software developers.

Which programming language should I learn first?

Python. It's the lingua franca of AI engineering. Every major framework (LangChain, LlamaIndex, CrewAI), every model API (OpenAI, Anthropic, Google), and every ML library runs on Python. JavaScript/TypeScript is a strong second choice for building web-based AI products. You don't need both to start — pick Python.

How long does it take to build a production-ready AI product?

A functional prototype: 1–2 weeks with the right tools. A production-ready system with evals, error handling, and monitoring: 2–3 months for a small team. The bottleneck isn't the AI — it's the engineering around it: data quality, edge case handling, deployment, and user experience.

Is AI engineering a bubble that's about to pop?

AI startups raised $238 billion in 2025 — 47% of all venture capital. Some of this is overheated. Companies like Builder.ai and Humane failed spectacularly. But the underlying demand is real: enterprise AI revenue hit $37 billion in 2025 (3x growth year-over-year), and coding tools alone generated $4 billion in spending. The bubble risk is in companies building undifferentiated "AI wrappers." The opportunity is in solving specific, measurable problems where AI creates genuine value.

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Where to Go from Here

The best way to learn AI engineering is to build something. Not tomorrow. Today. Here's a concrete 30-day plan:

- Week 1: Set up your environment. Install Python, get API keys for OpenAI and Anthropic (both offer free credits). Build a simple chatbot using the API.

- Week 2: Add RAG. Take a collection of documents (your notes, a company wiki, anything) and build a question-answering system over them using LangChain.

- Week 3: Add tools. Give your chatbot the ability to search the web, check the weather, or query a database. Learn function calling.

- Week 4: Add evals and ship. Write 20 test cases. Set up basic logging with Langfuse. Deploy your project (Streamlit + any cloud provider). Share it with someone who isn't you.

At the end of 30 days, you'll have built a working AI product with RAG, tools, and evaluations. That's more than most people who've been "following AI" for two years can say.

The field moves fast. Models that dominate today might be obsolete in six months. But the principles in this guide — choosing the right model, feeding it the right context, giving it tools, testing it rigorously, and shipping something real — those remain stable even as the technology beneath them evolves.

More people will talk about AI in 2026 than in any previous year. Fewer, proportionally, will build anything. The YC RFS list is open. The tools are free or cheap. The only barrier left is the decision to start.

Go build something people want.

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This guide is maintained by Turing College. Last updated: February 2026.