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Is your RAG (Retrieval-Augmented Generation) pipeline optimized, or is it even necessary? We all know context is king for LLMs, but large context windows might not be the answer. On Episode 63 of Tool Use, we deep dive into RAG with Apurva Misra, founder of Sentick. We explore the entire RAG workflow, from creating and optimizing embeddings to choosing the right vector DB (like Postgres with PG Vector). Apurva explains the critical role of re-rankers , the power of hybrid search (combining semantic and keyword search) , and when to consider agentic RAG. We also cover the essential steps for taking your RAG system to production, including data quality, feedback loops , and safety guardrails.
Connect with Apurva Misra:
LinkedIn: https://www.linkedin.com/in/misraapurva/
Consulting: https://www.sentick.com/
Website: https://apurvamisra.com/
Connect with us
https://x.com/ToolUsePodcast
https://x.com/MikeBirdTech
00:00:00 - Intro
00:01:18 - What is RAG (Retrieval-Augmented Generation)?
00:03:41 - Is RAG Dead? Large Context Windows vs. RAG
00:06:40 - The RAG Workflow: Embeddings, Chunking & Vector DBs
00:28:00 - Do You Need a Re-Ranker?
00:41:24 - Production RAG: Safety, Guardrails & Efficiency
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