Category
rag
3 articles
Guide to Using RAG with LangChain and ChromaDB/FAISS
TLDR: RAG (Retrieval-Augmented Generation) gives an LLM access to your private documents at query time. You chunk and embed documents into a vector store (ChromaDB or FAISS), retrieve the relevant chunks at query time, and inject them into the LLM's ...
A Beginner's Guide to Vector Database Principles
TLDR: A vector database stores meaning as numbers so you can search by intent, not exact keywords. That is why "reset my password" can find "account recovery steps" even if the words are different. 📖 Searching by Meaning, Not by Words A standard d...
RAG Explained: How to Give Your LLM a Brain Upgrade
TLDR: LLMs have a training cut-off and no access to private data. RAG (Retrieval-Augmented Generation) solves both problems by retrieving relevant documents from an external store and injecting them into the prompt before generation. No retraining re...