1. Executive Summary

This project demonstrates the design and implementation of a production-oriented Adaptive Retrieval-Augmented Generation (RAG) system focused on improving retrieval quality, reducing hallucinations, optimizing latency, and increasing observability across the entire retrieval-generation pipeline.

Unlike basic RAG implementations that rely on static dense retrieval and single-pass generation, this system introduces:

• Hybrid retrieval (Dense + BM25)
• Reciprocal Rank Fusion (RRF)
• Conditional cross-encoder reranking
• Semantic caching
• Adaptive query routing
• Hallucination detection loops
• Retrieval observability
• Query transformation retries
• LangSmith instrumentation

The system was engineered to simulate production AI system constraints including:

• latency optimization
• retrieval ambiguity
• cost efficiency
• reliability
• failure recovery
• operational monitoring

The knowledge base currently contains technical AI documents focused on: