RAG
5 results
How RAG Works: The Plain-English Guide to Retrieval Augmented Generation
RAG is the most widely used technique in production AI. Here's a clear, jargon-free explanation of how it works, why it matters, and when to use it.
Prompt Compression: How to Reduce Context Size Without Losing Quality
Long contexts cost money and degrade performance. Prompt compression techniques let you fit more relevant content into fewer tokens — here's what works in practice.
Retrieval Augmented Generation (RAG): Ground Your AI in Real Data
RAG connects an LLM to an external knowledge base so it answers from facts rather than memory. Learn how RAG works, when to use it, and how to prompt effectively in RAG systems.
Context Engineering for Agents
Context engineering is the discipline of deciding what information goes into an agent's context window, in what form, and when. It's the highest-leverage skill for building reliable agents at scale.
Prompting With Long Documents and Large Context
Pasting a 50-page document and asking 'what do you think?' rarely works. Learn how to structure prompts for long-form content, extract what matters, and work around context limits.