RAG for Your Docs

Stephen Blum · Intermediate ·🔍 RAG & Vector Search ·2w ago

Skills: RAG Basics85%

Key Takeaways

Builds a RAG pipeline using LangChain and Pinecone to answer questions over documents

Original Description

RAG, or Retrieval Augmented Generation, lets you talk to your own data. Think of it like Google search for your company or personal documents, with an AI that answers in plain text. You ask a question, like which LLMs are restricted because of compliance. The system searches your documents, pulls the most relevant passages, and adds them to your question. Then it sends that full prompt to the language model, which replies with a clear, human sounding answer based on what it found.

Watch on YouTube ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

More on: RAG Basics

View skill →

High Performance (Realtime) RAG Chains: From Basic to Advanced

High Performance (Realtime) RAG Chains: From Basic to Advanced

Coding the Ultimate RAG Engine from Zero

Coding the Ultimate RAG Engine from Zero

Building Agentic RAG From Scratch in Pure Python

Building Agentic RAG From Scratch in Pure Python

Build an LLM and RAG-based Chat Application using AlloyDB and LangChain

I Built a RAG App to Decode Airline Bureaucracy (So You Don't Have To)

I Built a RAG App to Decode Airline Bureaucracy (So You Don't Have To)

Akamai Developers

RAG Demo for Beginners: Full Hands-On Tutorial in Tamil | Build Your Own RAG AI | Karthik's Show

RAG Demo for Beginners: Full Hands-On Tutorial in Tamil | Build Your Own RAG AI | Karthik's Show

Related Reads

Inside RAG Chunking: Strategies, Trade-offs, and Engineering Lessons

Learn how to optimize Retrieval-Augmented Generation (RAG) chunking for large language models and understand the trade-offs and engineering lessons involved

Inside RAG Chunking: Strategies, Trade-offs, and Engineering Lessons

Learn how to optimize Retrieval-Augmented Generation (RAG) with chunking strategies and trade-offs for better performance

Medium · Data Science

RAG Architecture in Production: The Decisions That Actually Determine Quality

Learn the 7 key architectural decisions for a production-ready RAG system, beyond demo capabilities

Dev.to · Ama Senevirathne

RAG evaluation using ragas: a practical guide

Learn to evaluate RAG models using ragas in Python for improved performance and automation

Dev.to · Ayush Kumar

Build a Chatbot with RAG in 10 minutes | Python, LangChain, OpenAI