A wrong answer can come from bad retrieval or bad generation, and end-to-end evaluation can't always tell you which. Evaluate retrieval on its own first.

LLM generation cost is the number everyone tracks. It's rarely the biggest line item once you account for indexing, storage, and reranking at real query volume.

A character-count chunker will happily split a function in half. Code needs structure-aware chunking that respects syntax boundaries, not prose conventions.

A RAG index doesn't know a document is outdated unless something tells it. Here's how to build that signal in, instead of discovering staleness from a user complaint.

Logging the question and the answer tells you a RAG pipeline ran. It doesn't tell you why a specific answer was wrong. Here's what to log to actually debug retrieval quality over time.

A third framework enters the ring. Here's how Microsoft's AutoGen compares to LangGraph and CrewAI, and a decision framework for choosing between all three.

The question a user types and the query that retrieves the right documents are often not the same string. Query rewriting closes that gap before retrieval, not after.

Some questions can't be answered by a single retrieval call, because the second search depends on what the first one finds. Multi-hop retrieval makes that dependency explicit.

Embedding a table row-by-row loses the structure that made it a table. Structured data needs a different retrieval strategy than prose.

Caching retrieval results is an easy latency and cost win, right up until the underlying documents change and the cache doesn't know.