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From prototype to production

Build production AI systems.

Free, practical guides for engineers who ship AI. Learning paths, labs and projects that take an idea from a notebook to a system that is deployed, traced and evaluated.

Start the AI Engineer roadmap Browse learning paths
  • LLMs
  • RAG
  • Agents
  • MCP
  • Evals
  • Tracing
  • Guardrails
  • Cost

New to AI? Start with Everyday AI →

support-agent / answer
Production
  1. retrievevector search, k=5142 ms
  2. rerankcross-encoder38 ms
  3. generatestructured output910 ms
  4. guardrailsPII and policy checks21 ms
Groundedness eval passedCost tracked
Example trace. You build one like it in the labs.

The Learning Stack

Five stages, from first prompt to shipped system

  1. 01 / UNDERSTAND

    Learn

    Build a strong foundation in LLMs, retrieval, context and model behaviour.

  2. 02 / IMPLEMENT

    Build

    Turn concepts into working applications, workflows and agent patterns.

  3. 03 / PRODUCTION

    Engineer

    Design for reliability, evaluation, security, latency and scale.

  4. 04 / OBSERVE

    Measure

    Track quality, cost and latency, and the outcomes that matter.

  5. 05 / DELIVER

    Apply

    Use repeatable patterns to solve real problems in your domain.

What you'll build

Portfolio projects that behave like production

Each project ends with something deployed, traced and evaluated, plus a write-up of its limits.

All projects

Go beyond the notebook

The demo works. Now make it hold up.

We teach the engineering around the model: tracing every step, gating answers on evals, handling failure and keeping cost in check.

  • Trace each retrieval and generation step
  • Block ungrounded answers with an eval gate
  • Measure latency and cost per request
agent.py
from opentelemetry import trace

tracer = trace.get_tracer("support-agent")

def answer(question: str) -> str:
    with tracer.start_as_current_span("retrieve") as span:
        docs = retriever.search(question, k=5)
        span.set_attribute("retrieval.hits", len(docs))
    with tracer.start_as_current_span("generate"):
        reply = llm.generate(build_prompt(question, docs))
    # Eval gate: an ungrounded answer never reaches the user.
    require_grounded(reply, docs)
    return reply

Learning paths

Pick the role you're working towards

Structured journeys from software developer to AI Engineer, Agentic AI Engineer and Forward Deployed Engineer.

Compare every path

New to AI? Start with Everyday AI.

Plain-English guides, prompts you can copy, and honest comparisons. No coding needed.

Start with Everyday AI

Tutorial series

Go deep, one series at a time

Several chapters, labs and a capstone per series.

All series

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What's new on Learn AI Engineering

Free tools

Plan, cost and choose before you build

No sign-up needed. Estimates show their assumptions so you can check them.

About us

An independent publisher for people who ship AI

Learn AI Engineering is an independent publisher. We write practical guides for engineers who take AI systems from prototype to production, and plain-English guides for everyone who uses AI at work and at home.

We are not affiliated with any AI provider. We explain trade-offs, show our sources and date anything that changes fast, such as prices and model limits.

More about us →How we test and review →

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Questions about learning here

Where should I start learning AI engineering?

Start with the AI Engineer learning path. It connects foundations, LLM applications, retrieval, agents and evaluation with labs and projects. If you already know Python and HTTP APIs, use the series prerequisites to choose your starting point.

Is the learning material free?

The tutorials, labs and project guides are available without payment or an account. Learning progress is saved in your browser, so clearing browser storage resets that progress.

How can I practise production AI engineering?

Use the labs for guided implementation, the challenges to diagnose failure cases, and the projects to assemble a larger system. Evaluate your implementation and document its limitations before using it with real users.

Can I learn to use AI here if I am not an engineer?

Yes. The Everyday AI section teaches prompting, choosing a model and building simple automations without code, with a seven-day starter plan and copy-ready prompts. A BioTech hub covers responsible use in life sciences roles.

Does completing a roadmap guarantee a job?

No. The roadmaps provide a learning sequence and estimated study time. Your progress depends on your starting skills and practice. Build and explain your own projects to demonstrate what you have learned.

Explore the AI engineer learning path →