A practical blueprint for building scalable, safe AI support chatbots—from NLU and RAG to orchestration, guardrails, and observability.
Practical strategies to optimize LLM context windows—reduce cost and latency while preserving accuracy with RAG, chunking, compression, caching, and evaluation.
A clear, practical guide to Mixture-of-Experts (MoE) architecture: routing, experts, training stability, distributed systems, and when to use it.
A practical, code-first guide to Anthropic’s Claude Message Batches API: limits, pricing, prompt caching, 300k-token outputs, and production patterns.
A practical blueprint for deploying autonomous AI agents to production—architecture, safety, reliability, evals, cost control, and ops patterns.
A practical guide to function calling vs. tool use in LLMs: architectures, trade-offs, design patterns, reliability, security, and evaluation.
