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.
A practical, data-driven guide comparing prompting vs. fine-tuning for LLM apps, with decision checklists, trade-offs, and implementation tips.
A practical, end-to-end tutorial for generating, evaluating, and governing synthetic data for ML using Python, SDV, and sdmetrics.
