Why writing culture matters more in the AI era: LLM agents are “literal readers” that do exactly what your docs say and fill in none of the gaps a human silently patches. Built on a shared STATUS.md, the case for writing things down, and where it tips into over-documenting.
A 1M-token LLM context window is a tool, not a target. How context actually works, why long threads cost more on every turn, and when to start a fresh chat versus keep going, with practical Claude Code tips and /context.
A file-based pattern for coordinating multiple LLM agents on one task: a single shared, per-feature STATUS.md the agents read and write under explicit rules, how to make them follow a protocol and survive context loss, and when not to use it.
Building a minimal LLM Ops stack: a FastAPI “customer support reply drafter” instrumented with Langfuse for request tracing, grounded retrieval, and per-request model cost tracking, so every LLM call is inspectable.
Retrieval-Augmented Generation (RAG) explained without buzzwords: how it gives an LLM the right data at answer time to fix stale knowledge and hallucinations, the step-by-step flow, its benefits over fine-tuning, and when RAG is not the answer.