For AI engineers

Build first. Read the chapter when it breaks.

You learn from the docs and the model. When the build breaks in a way neither can explain, take the course by the engineer who already fixed it. From $10 a month.

Start Your Free TrialMeet the Experts

Free for 7 days. Valid card required. Cancel anytime.

Rothman
Cover image for RAG-Driven Generative AI
Kuligin
Cover image for Architecting Generative AI Applications
Labonne
Cover image for LLM Engineer's Handbook
Ahmad
Cover image for 30 Agents Every AI Engineer Must Build
Alto
Cover image for AI Agents in Practice

Sound familiar?

You bought the course. Then LangChain shipped 1.0.

Its chains and agents moved to langchain-classic, and half the tutorials you saved broke overnight. Every title here carries the month it came out, so you can check it first.

This course has an updated version code dated June 2024. But it is still outdated.

A LangChain learner on r/LangChain, December 2024
  1. 1

    Model Context Protocol

    Nov 2024

    Cover image for Model Context Protocol for LLMs
    Model Context Protocol for LLMsFeb 2026
  2. 2

    DeepSeek-R1

    Jan 2025

    Cover image for DeepSeek in Practice
    DeepSeek in PracticeNov 2025
  3. 3

    Claude Code 1.0

    May 2025

    Cover image for Practical AI Development Workflows with Claude Code
    Practical AI Development Workflows with Claude CodeJun 2026
  4. 4

    LangChain 1.0

    Oct 2025

    Cover image for Agentic AI Systems: Build & Deploy with LangChain/LangGraph
    Agentic AI Systems: Build & Deploy with LangChain/LangGraphApr 2026
130AI titles released since LangChain 1.0

Does it go past the demo?

The part after the demo

You've already built the chatbot over a PDF. Our experts write about what happens once real users hit it.

traces · support-agent · production

POST /support/answer

4.82 s · 12,480 tokens

  • POST /support/answer

    4.82 s

  • Where the course stopped

    • embed_query

      120 ms

    • vector_search0 of 5 relevant1

      260 ms

    • llm.generate2,680 tokens

      1.10 s

  • tool.get_ordertimeout2

    2.00 s

  • tool.get_orderretry 1

    420 ms

  • llm.generate9,800 tokens3

    780 ms

  • eval.groundedness0.424

    130 ms

76%of our AI titles are written for engineers past the basics
  1. 1

    Retrieval that misses

    Five chunks come back. None of them holds the answer.

    Cover image for RAG from First Principles
    Chapter 7 · 2026Retrieval Post-ProcessingRAG from First Principles
  2. 2

    Tools that time out

    The order API hangs for two seconds. The agent retries it blind.

    Cover image for Agentic Architectural Patterns for Building Multi-Agent Systems
    Chapter 7 · 2026Robustness and Fault Tolerance PatternsAgentic Architectural Patterns for Building Multi-Agent Systems
  3. 3

    A bill for every answer

    Two model calls and 12,480 tokens to answer one customer.

    Cover image for Production LLM Monitoring: Observability, Tracing & Cost Optimization
    Chapter 6 · 2026Cost Optimization Strategies That WorkProduction LLM Monitoring: Observability, Tracing & Cost Optimization
  4. 4

    Answers nobody checks

    It scores 0.42 on groundedness. It ships to the customer anyway.

    Cover image for AI Agent Evals and Reliability - Evaluations and Observability with Galileo AI
    Chapter 3 · 2026LLM / Agent EvaluationsAI Agent Evals and Reliability - Evaluations and Observability with Galileo AI

Who wrote it?

Learn from the engineers who wrote the handbook

Our experts build and ship LLM systems for a living. They write about the parts that break after the demo.

  • Cover image for LLM Engineer's Handbook
    Maxime Labonne, author of LLM Engineer's Handbook

    Maxime Labonne

    70k

    He and Paul Iusztin take one LLM app from data to deployment.

    3 titlesProfile
  • Cover image for RAG-Driven Generative AI
    Denis Rothman, author of RAG-Driven Generative AI

    Denis Rothman

    The 2026 edition adds GraphRAG and multimodal video pipelines.

    7 titlesProfile
  • Cover image for AI Agents in Practice
    Valentina Alto, author of AI Agents in Practice

    Valentina Alto

    A Microsoft AI architect on building agents with memory and tools.

    5 titlesProfile
  • Cover image for 30 Agents Every AI Engineer Must Build
    Imran Ahmad, author of 30 Agents Every AI Engineer Must Build

    Imran Ahmad

    Thirty agent designs, each one built and explained. Published in 2026.

    4 titlesProfile
Meet the Experts

Why not just ask the model?

The agent loops and nobody can say why

Keep building with the assistant open. When the same tool call fails for the fourth time, read the chapter by someone who has fixed it.

Your agent, this afternoon
planget_order()errorretry4th try

Same call, same error

ExpertEdge
Cover image for Design Multi-Agent AI Systems Using MCP and A2A
Chapter 10Testing, Debugging, and Troubleshooting Multi-Agent SystemsDesign Multi-Agent AI Systems Using MCP and A2A, 2026

A whole chapter on debugging agents that call tools and each other.

75titles on building and running AI agents

Does it cover my stack?

Use the model your company already picked

OpenAI, Claude, Bedrock or a Llama on your own box. ExpertEdge doesn't sell a model, so it has no favourite to push. The library has courses on each one.

Model providerAmazon Bedrock

Is it worth the money?

Cheaper than one cohort

An evals cohort can cost $3,500. The same money buys decades of the whole library.

One evals cohort

$3,500

Price quoted on Hacker News, September 2025

29years of the whole library, at today's annual priceExpertEdge, billed yearly · $120 a year

Where does it lead?

Every skill the role asks for, laid out

Set AI Engineer as your target. ExpertEdge lines up the skills it asks for, from retrieval to evals, and shows you which one comes next.

  1. What you've shipped

    Where you are

  2. AI Safety, Evaluation & Guardrails

  3. LLM Application Development

  4. MLOps & Model Deployment

  5. Model Fine-Tuning & Adaptation

  6. Prompt Engineering

  7. Supervised & Unsupervised Learning

  8. Vector Databases & Embeddings

  9. AI Engineer

    The goal

What's the catch?

What ExpertEdge isn't

  • A cohort

    No live sessions and no office hours. You read when the build needs it.

  • A hosted lab

    We don't host notebooks to shift-enter through. You run it on your own stack.

  • A forum

    No forum or Q&A thread. Your team and the issue tracker still do that job.

  • Another intro course

    We won't explain what a token is. Most of our AI titles start well past the basics.

You bring the project and the API key. We'll bring the chapter for when it breaks.

And the price?

One price, everything included

Monthly

Billed monthly

$15per month

Everything, month to month. Cancel anytime.

What's included

  • Every LLM, RAG and agent title
  • The whole 3,400+ course library
  • Skills and a passport that stay yours
Start Free Trial

Annual

Billed yearly ($120)

$10per month

Everything for a third less, paid once a year.

What's included

  • Every LLM, RAG and agent title
  • The whole 3,400+ course library
  • Skills and a passport that stay yours
Start Free Trial

No setup fees, no minimum seats. Invoicing available on plans of 20+ seats or more.

Comparing options? Read how ExpertEdge stacks up against Udemy Business, LinkedIn Learning, Coursera and edX in our comparisons

Our blog

Built by people who know how great teams run L&D programs that work at scale

Still not convinced ExpertEdge is the right fit for you? Review some of our thinking on enterprise learning, content strategy and more. See if we align via the full blog.

Content Strategy

The B2B learning content stack most enterprises miss

Enterprise L&D teams have approached content procurement the wrong way for a decade. What a mature B2B learning content stack looks like, and the layers most enterprises are missing.

Oli Huggins

Oli Huggins

CEO and Founder

Comparisons

ExpertEdge vs Coursera and edX for B2B learning

A side-by-side of Coursera, edX and ExpertEdge for enterprise learning content: what each does well, where each falls short, and how to choose.

Oli Huggins

Oli Huggins

CEO and Founder