AI engineering
LLM apps that hold up in production: RAG, agents, evals, gateways and guardrails.
6 ready to open
How an LLM works, for engineers
Tokens, the context window, sampling and why models make things up — the mental model every other topic builds on.
The architecture of an AI app
Every piece between a user's question and a reliable answer: API, orchestration, retrieval, tools, the model gateway, streaming, evals and observability.
RAG, end to end
Ingest, chunk, embed, retrieve, re-rank — and how to know it's actually finding the right thing.
Prompts, context and structured outputs
What goes into the context window, in what order — and getting JSON you can trust back out.
Agents, tools and MCP
The loop where a model calls tools, when a fixed workflow is better, MCP as the plug for tools, and how to keep it from running away.
Evals: testing what an LLM says
Datasets, graders and regression gates for output that's never exactly the same twice.
- Choosing a model: quality, cost, latency
- LLM gateways: retries, fallbacks and resilience
- Guardrails and prompt injection
- Observability for LLM apps
- Prompting vs RAG vs fine-tuning
- Agent memory
- Designing the experience of an AI feature