A practical guide to building robust multi-step AI pipelines with prompt chaining, orchestration patterns, state, testing, guardrails, and cost control.
Build end-to-end observability for AI agents: traces, metrics, logs, and evals to debug, govern privacy, and scale quality, reliability, and cost.
A practical playbook to optimize retrieval pipelines for search and RAG: metrics, chunking, hybrid retrieval, ANN tuning, re-ranking, and efficiency.
A practical, end-to-end guide to detecting and mitigating hallucinations in LLM outputs, from uncertainty signals to retrieval-based verification.
A practical guide to designing, implementing, and governing AI chatbot personality customization—traits, prompts, memory, guardrails, and evaluation.
Design and ship a production-grade AI auto-tagging classification API: models, thresholds, architecture, evaluation, security, and scaling best practices.
