Building a Simple RAG Application: From Fundamentals to Implementation
summary
Hands-on demo builds a Retrieval-Augmented Generation pipeline end-to-end using OpenAI GPT-3.5-turbo for generation and embeddings and Pinecone vector DB for retrieval, loading a prepared AI-papers dataset, chunking text, encoding and upserting (first 500 vectors upserted as a smoke test then the full set). Retrieval returns top-K chunks which are concatenated into prompts; system works but is noisy — author reports roughly 60–70% accuracy out of the box and explores classification, reranking and human-in-the-loop evaluation to improve it.
AI-written summary · about
chapters (6)
- 0:00Building a simple RAG app
- 7:25Why RAG systems and limits
- 25:16Knowledge base with Pinecone
- 48:24Retrieval and generation workflow
- 1:02:33RAG challenges and improvements
- 1:16:31Q&A and exercises
related talks
21mHow RAG is Redefining AI Capabilities
44mBuild RAG from Scratch
01h 17mGraph Database and Retrieval Augmented Generation
15mEvaluating RAG Model Performance Metrics, Bias, and Interpretability
53mGenerative AI: Overview and use cases
23m"Deliver AI at Lightspeed: Streamlined approach to AI strategy"
37mBuilding Generative AI Applications
41mGenAI, vector/semantic/hybrid search, RRF, NLP, (...) and other buzzwords