What You’ll Learn
By the end of this module, you’ll be able to:- Implement two-stage retrieval (first-pass + reranking) pipelines
- Choose between full-text, semantic, and hybrid search strategies
- Optimize chunking strategies for different document types
- Process unstructured data (PDFs, images) reliably
- Evaluate retrieval and generation quality independently
- Debug RAG failures using component-level analysis
Why RAG Matters in Production
RAG (Retrieval-Augmented Generation) is the workhorse of production AI systems. Here’s why:- Adoption: Many enterprises favor RAG to access current, proprietary data; reported adoption varies by survey (2024–2025).
- It solves the knowledge problem: LLMs are frozen in time at training. RAG connects them to current, proprietary data
- Cost-effective: Update your knowledge base without retraining; fine-tuning can incur significant one-time and ongoing costs depending on model size and infrastructure.
- Grounded responses: RAG reduces hallucinations by constraining generation to retrieved context
Real-World Context
Case Study: Legal Document Analysis A law firm tried using GPT-4 directly to answer questions about case law. Problems:- Hallucinated legal precedents (dangerous!)
- Couldn’t access firm’s proprietary case notes
- No way to cite sources or verify claims
- Retrieved relevant cases and notes from their database
- Constrained LLM to only use retrieved context
- Generated answers with citations to source documents
- Result: 95% accuracy, full auditability, zero hallucinations on verified cases