— Techniques
RAG (Retrieval-Augmented Generation)
Giving an AI access to your private documents so it can answer questions about them.
Also known as: Retrieval Augmented Generation · Document Q&A
What is RAG (Retrieval-Augmented Generation)?
RAG (Retrieval-Augmented Generation) is the technique of giving an LLM access to specific documents at query time. The flow: documents are embedded into vectors, stored in a vector database, retrieved on each query based on relevance, and stuffed into the prompt before the LLM responds. RAG turns an LLM into a private knowledge assistant. ONROL teaches RAG on Day 2 of the Generalist track using Supabase pgvector + Claude.
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ONROL pages on RAG (Retrieval-Augmented Generation)
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Terms connected to RAG (Retrieval-Augmented Generation)
Techniques
Embedding
A numeric representation of text (or image) that captures its meaning.
Open →Tools
Vector Database
A database optimised for storing and searching embeddings by similarity.
Open →Techniques
Fine-Tuning
Adjusting a pre-trained AI model on your specific data to change its behaviour.
Open →Concepts
Context Window
The maximum text an LLM can read in one prompt.
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