Python
Tutorial series
AI Engineering Foundations
Python, APIs, HTTP and the engineering craft behind every AI system
Building LLM Applications
Tokens, context windows, prompting, structured outputs and function calling
Production RAG
Build retrieval systems that actually work in production
Building AI Agents
Give LLMs tools, memory and the ability to act autonomously
Agentic Workflows
State machines, graphs, multi-agent systems and MCP
LLM Evaluation
Measure retrieval, generation, agents and production quality
FDE Engineering Fundamentals
Discovery, solution architecture, POCs and productionisation
Labs
Build a Production HTTP API
Implement a POST /api/echo endpoint that validates input, returns structured output, and handles errors with proper status codes.
Call an LLM with Structured Output
Implement call_structured() that returns a validated Pydantic model from an LLM, with retry on validation failure.
Document Chunking Strategies
Implement three chunking strategies and measure their impact on a sample retrieval eval.
Embeddings & Vector Search
Implement a VectorStore with embed, insert, and search(filter) methods.
Reranking for Retrieval Precision
Retrieve top-20 with vector search, rerank to top-5, and measure precision@5 improvement.
Build a ReAct Agent Loop
Implement run_agent(task, tools, max_iter) that loops reasoning + tool calling until final answer or max_iter.
Add Tracing & Observability
Instrument call_llm, retrieve and tool_call with spans. Export to a trace backend.
Build an Evaluation Pipeline
Implement run_eval(golden_set, prompt_version) -> Report with faithfulness and answer_relevance scores.
Projects
AI Chatbot
Your first production LLM application
Document Summariser
Summarise long documents with chunking and map-reduce
RAG Assistant
Production retrieval-augmented generation over your documents
Research Agent
Autonomous multi-step research with tool calling
Multi-Agent Research Platform
Specialised agents orchestrated to solve complex research
Enterprise Knowledge Assistant
FDE capstone: ship an enterprise AI assistant end-to-end