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Saram ConsultingThoughts and strategies on the intersection of Biotech, Artificial Intelligence, and Regulatory Compliance.
A technical deep-dive into the three layers of LLM caching — prompt prefix, semantic, and KV-cache — with production benchmarks, provider pricing, and an implementation playbook that takes cache hit rates from single digits to 80%+.
Read Article →Building your own AI gateway is one of the most practical infrastructure investments in the AI stack. Here's the verified architecture, the tools that actually exist, and how to make it work in regulated environments.
Read Article →Five cost-optimization principles — better defaults, routing, caching, lean context, and visibility — translated into a validated, audit-ready LLM gateway architecture for regulated life sciences. Compliance is not a bolt-on; it is the routing logic.
Read Article →A complete, production-ready tutorial for building a hybrid retrieval-augmented generation pipeline — vector search plus BM25 keyword matching — using four Docker containers, local embeddings, and any OpenAI-compatible LLM.
Read Article →Standard RAG chunking destroys SOP context. Here's the parent-child chunking architecture, dual-engine table extraction, and prompt engineering patterns that make regulatory document retrieval actually work.
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