RAG System for Enterprise Knowledge Management
Production RAG with 73% hallucination reduction and sub-second response times
The Challenge
Building a RAG system that actually works in production - solving hallucination, latency, and the “context window puzzle” while maintaining factual accuracy.
Innovation
Multi-stage retrieval pipeline with intelligent optimizations:
- Adaptive Chunking: Different strategies for code (1024 tokens) vs FAQs (256 tokens)
- Hybrid Search: Combines semantic embeddings with BM25 keyword matching
- Citation Verification: Post-generation validation catches hallucinated sources
- Smart Caching: Semantic similarity cache with 84% hit rate
Results
- 84% F1 score on retrieval accuracy
- 73% reduction in hallucination through citation verification
- 0.9s response time (down from 1.4s through caching)
- 90% cost savings vs commercial APIs ($3 vs $30 per million tokens)
Technical Implementation
class HybridRetriever:
def retrieve(self, query, top_k=5):
# Semantic search via embeddings
semantic_scores = cosine_similarity(query_embedding, doc_embeddings)
# Keyword search via BM25
keyword_scores = self.bm25.get_scores(query.split())
# Intelligent fusion based on query type
if self._is_technical_query(query):
final_scores = semantic_scores * 0.5 + keyword_scores * 0.5
else:
final_scores = semantic_scores * 0.8 + keyword_scores * 0.2
Key Learning
In RAG systems, retrieval quality matters more than generation model sophistication. “Garbage In, Garbage Out” at the retrieval stage determines overall system performance.