Designing effective retrieval systems

About this Video

• Video Title: Designing effective retrieval systems
• Channel: Gooru Content
• Speakers: (Speaker name not available in transcript)
• Duration: 00:05:13

Introduction

This video explains Retrieval Augmented Generation (RAG) and how it improves the accuracy and relevance of generative AI in the workplace. It highlights the limitations of Large Language Models (LLMs) and shows how RAG addresses these limitations by grounding AI in factual data.

Key Takeaways

• LLMs have limitations: While LLMs excel at creativity and fluency, they can struggle with accuracy, especially with niche topics or current information; they can also hallucinate.
• RAG bridges the gap: RAG combines the generative power of LLMs with the factual accuracy of external knowledge bases. It retrieves relevant information based on user queries, augments the LLM prompt with this information, and ensures more accurate and relevant responses.
• Building a robust retrieval system is crucial: This involves meticulous knowledge base preparation (data ingestion, text extraction/cleaning, chunking, metadata integration), a well-defined indexing strategy (using vector databases and embeddings), and choosing appropriate retrieval methods (vector search, keyword search, query expansion, reranking).
• RAG's applications: RAG can improve internal Q&A systems (faster, more accurate answers to employee questions), customer support (faster resolution times, higher first-contact resolution, consistent messaging), and generally increase productivity and efficiency.
• Getting started with RAG: Begin with open-source frameworks (Langchain, Llama Index), test with a low-risk use case, focus on data quality and chunking, and continuously evaluate and iterate.