The Engineering Behind Enterprise AI

  • RAG Evaluation Techniques — A Field Guide for Multi-Turn Voice Agents

    RAG Evaluation Techniques — A Field Guide for Multi-Turn Voice Agents   How to rigorously evaluate retrieval-augmented generation (RAG) in conversational voice agents — distinct from simple document Q&A interfaces. Written from real-world implementation experience with production voice systems.   All examples use the fictional company FleetPulse (fleet telematics provider) to illustrate concepts without referencing…

    Amit Patriwala

  • Loops: The Quiet Skill Behind Every AI System That Actually Scales

    Why the Future of AI Isn’t About Better Models—It’s About Better Loops   Every week a new AI model arrives. A larger context window. A better benchmark score. A more impressive demo.   The industry conversation usually follows the same pattern: Is GPT-5 better than Claude? Is Claude better than Gemini? Is Gemini better than…

    Amit Patriwala

  • The Hidden Context Window Problem in RAG Systems: A Real Production Incident with vLLM and Qwen3

    When Your 32K Context LLM Fails at 4K Tokens: A Production vLLM Troubleshooting Guide   One of the most common misconceptions in Generative AI systems is: “The model supports 32K context, so my application automatically supports 32K context.”   In production, that assumption can lead to unexpected failures.   Recently, we encountered a production issue…

    Amit Patriwala