Introduction

RAG reduces hallucination and keeps answers aligned with your data. In production, that means thoughtful chunking, retrieval tuning, and continuous evaluation. We distill practices that work across docs, code, and mixed content.

Chunking and Embeddings

Chunk strategy Size, overlap, and semantic boundaries affect recall and noise. We compare fixed-size, sentence-aware, and section-based chunking and when to use each.

Embedding and indexing Model choice and indexing (dense, hybrid, reranking) directly impact latency and accuracy. We outline a simple pipeline and how to iterate without rebuilding everything.

Retrieval and Generation

Query expansion and reranking Single-query retrieval often misses. We show how to expand queries and use a reranker to improve precision before the LLM step.

Evaluation and monitoring We cover relevance metrics, answer faithfulness, and how to log and monitor so you notice regressions before users do.