Learn AI Engineering.
Build production AI systems.
Structured learning paths, architecture guides, engineering challenges and portfolio projects covering LLMs, RAG, AI agents, evaluation, observability and Forward Deployed Engineering.
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01 — Choose Your Path
Four tracks, one destination
Structured learning journeys from software developer to AI Engineer to FDE.
From software developer to AI Engineer
Master generative AI systems
Build autonomous AI systems
Forward Deployed Engineering
Run the shared AI infrastructure every team builds on
Choose, govern and scale AI for measurable value
02 — Featured Series
Premium tutorial series
Series-first learning. Multiple chapters, labs and a capstone per series.
Understand LLMs from the inside out: tokenisation, context windows, prompting, structured outputs, function calling, streaming and multimodal inputs. The foundation for everything generative.
End-to-end production RAG: document ingestion, chunking, embeddings, vector search, retrieval, reranking, evaluation and production architecture. From naive chatbot to reliable knowledge system.
Build real agents: tool calling, the ReAct loop, agent memory, planning, error recovery and production agent patterns. Move from single-shot completions to autonomous systems that act.
Move beyond single agents. Build graph-based workflows, multi-agent systems, planning pipelines, human-in-the-loop checkpoints, and integrate tools via MCP.
03 — Interactive Labs
Learn by building
Hands-on labs with starter code, hints and validation steps.
Implement a POST /api/echo endpoint that validates input, returns structured output, and handles errors with proper status codes.
Implement call_structured() that returns a validated Pydantic model from an LLM, with retry on validation failure.
Implement three chunking strategies and measure their impact on a sample retrieval eval.
Implement a VectorStore with embed, insert, and search(filter) methods.
04 — Build Something
Portfolio-grade projects
From beginner chatbots to FDE capstones: build, deploy and demonstrate.
Create an AI chatbot that can hold a coherent conversation, maintain history, stream responses, and refuse harmful requests.
Summarise 50-page documents within token limits while preserving key points, entities and structure.
Answer questions over a private document set with citations, confidence scores, and graceful 'I don't know' behaviour.
The query is embedded and used to search a vector database for relevant chunks. Candidates are reranked by a cross-encoder, the top results become the LLM context, and the model generates a grounded answer with citations.
Your RAG system over internal docs has excellent retrieval scores (recall@5 = 0.91) but users complain the answers are wrong, vague or cite the wrong source. Diagnose the problem.
A tier-1 bank wants an AI assistant over internal policies, procedures and customer-support documentation. Support agents should get instant, cited answers to policy questions. Compliance must be able to audit every query and answer. Data access must respect role-based permissions.
05 — Latest Chapters
Fresh from the curriculum
Recently published chapters across series.
Every learning experience moves you from concept to portfolio.
Not a blog. Not a course. A connected engineering curriculum that takes you from software developer to production AI Engineer to Forward Deployed Engineer.
Learn
Understand the concept
Build
Guided implementation
Experiment
Modify and observe
Ship
Production patterns
Demonstrate
Portfolio project
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.
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.