Advance your RAG pipeline by integrating hybrid retrieval methods that combine BM25 and embeddings, implementing iterative retrieval with query refinement, and summarizing multiple context chunks when needed. Learn to constrain LLM outputs to rely strictly on retrieved context, and apply advanced error handling, fallback strategies, and logging to ensure accuracy and reliability in your system.
Overview
Syllabus
- Unit 1: Constrained Generation in Retrieval-Augmented Systems
- Implementing the Base Prompt
- Mastering the Strict Prompt Strategy
- Citation Prompt Strategy Implementation
- Context Length Management
- Constrained Generation from Scratch
- Unit 2: Iterative Retrieval for Enhanced RAG Pipelines
- Inverted Distance Similarity Score
- Limit Chunks in Iterative Retrieval
- Enhance Iterative Retrieval Process
- Unit 3: Handling Overlaps and Summarization in RAG Pipelines
- Detecting Overlapping Text Chunks
- Fix the Summarization Logic
- Crafting the Final Answer
- Unit 4: Combining Lexical and Embedding-Based Retrieval in RAG Systems
- Enhance Hybrid Retrieval Function
- Fix BM25 Normalization Bug
- Refine Your Retrieval System
- Building a BM25 Index
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