RAG for Engineers - Chunking, Embeddings and Evaluating Retrieval
The practical decisions in a retrieval pipeline: how to chunk, which embedding choices matter, hybrid search, and how to measure retrieval separately from answers.
Most RAG systems that disappoint fail at retrieval, not generation. The model is fine, but it was handed the wrong passages. This guide walks through the pipeline stages where quality is won or lost, and how to measure each one so you stop tuning by anecdote. If you are new to the idea, read our RAG for beginners guide first.
Related guides
How AI Assistants Actually Work (Without the Jargon)
A plain-English tour of what happens when you type a message to an AI assistant, and why that explains both its strengths and its odd mistakes.
4 min read
Writing Your First Good Prompt
A simple five-part recipe for prompts that get useful answers on the first try, with before-and-after examples you can adapt to any assistant.
4 min read
Prompt Patterns That Work Across Every Assistant
Seven reusable prompting patterns, from role and few-shot examples to critique loops, with templates you can paste into any chatbot.
5 min read