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The article debunks five common AI‑tool hot takes, arguing you still must read AI‑generated code, AI fluency matters in hiring, MCP and Skills serve different purposes, RAG remains useful, and needing fine‑tuning signals a messy codebase. It offers concrete rules for reviewing generated code and integrating AI components responsibly.
The post explains how retrieval‑augmented generation (RAG) lets LLM‑based assistants answer questions from private corpora. It covers chunking documents into passages, embedding queries and chunks, similarity metrics, and the trade‑offs of different vector indexes (flat, IVF, HNSW). The focus is on practical design choices rather than new research.
The article walks through building a TypeScript middleware that validates RAG citations by checking exact byte‑span offsets against source buffers, replacing fuzzy heuristics with deterministic guarantees. Benchmarks show sub‑millisecond verification for 1,000 citations across 50 documents.
