Analyzing the intersection of Distributed Systems and Generative AI. Focusing on real-world implementation challenges: latency, evaluation, and operational rigour.
Why high-accuracy retrieval means nothing if your users leave before the answer loads. Techniques from Cache-Augmented Generation (CAG) to Hybrid Search that achieved 40% latency reduction.
Moving beyond academic benchmarks (MMLU) to business-centric metrics. Implementing "LLM-as-a-Judge" patterns and RAGAs framework for production-grade evaluation.
Retrieval strategies, chunking, hybrid search, and latency optimization.
Benchmarking, LLM-as-Judge, RAGAs metrics, and quality assurance.
Prompt caching, model selection, and token usage reduction.
Production deployment, scaling, and operational best practices.
I'm always open to discussing AI implementation challenges and solutions.