Technique Guide
Retrieval-Augmented Generation
RAG connects AI models to external knowledge — your documents, databases, and APIs — so they can answer questions grounded in real data, not just training memory. It's the most widely used technique for building production AI systems.
What is RAG?
Retrieval-Augmented Generation (RAG) is a pattern where, instead of asking an AI model to answer from its training data alone, you first retrieve relevant documents from a knowledge base, then inject them into the prompt as context.
The basic flow: user asks a question → system retrieves relevant document chunks → retrieved chunks are added to the prompt as context → model answers using both the context and its training knowledge.
RAG prompt structure
Use the following documents to answer the question.
Only use information from the provided documents.
If the documents don't contain the answer, say so.
[Document 1]: ...
[Document 2]: ...
Question: [user question]
Key RAG Prompting Techniques
Constrain the model to the retrieved context
"Only use information from the provided documents. If the answer is not in the documents, say you don't know." This prevents the model from hallucinating beyond what was retrieved.
Ask the model to cite its sources
"For each claim, cite which document it comes from using [Doc N] notation." Citations make it easy to verify the output and identify retrieval failures.
Handle the no-answer case explicitly
"If the retrieved documents don't contain enough information to answer, say so clearly rather than guessing." Without this, models often synthesize a plausible-sounding but fabricated answer.
Ask for confidence when relevant
"Rate your confidence (high/medium/low) based on how directly the retrieved documents address the question." Useful for downstream quality filtering.
Articles
Core Concept
How RAG Works: A Complete Technical Walkthrough
The full RAG pipeline — embedding, retrieval, context injection, and generation — explained clearly.
Foundations
What is Context Engineering?
RAG is context engineering. Learn the broader discipline of managing what goes into the AI's context window.
Advanced
Chain of Thought Prompting
Combine CoT with RAG for better reasoning over retrieved content.
Foundations
What is Prompt Engineering?
The foundational context for understanding how prompts shape RAG behavior.
Related Lessons
Structured lessons on RAG and the techniques that work alongside it.
Related Guides
Learn RAG in Context
The Intermediate track covers RAG alongside XML tags, long documents, and other production techniques.
Go to RAG Lesson