Production RAG: Overcoming Hallucinations in Enterprise AI Workflows
Karan MehtaHead of AI Engineering
Naive vector search RAG systems frequently fail in production due to chunking boundary loss, outdated knowledge graphs, and context dilution. Here is how we engineered high-precision semantic retrieval for enterprise clients.
Key Architectural Takeaways
Discuss Your Project- Hierarchical parent-child document chunking preserves document context without blowing token budgets.
- Hybrid keyword (BM25) and dense vector retrieval boosted recall accuracy to 96.4%.
- Reranking models (Cohere Rerank) filter out irrelevant passages before feeding the LLM prompt.
#AI#LLMs#RAG#Vector DB#Embeddings