A practical, end-to-end guide to detecting and mitigating hallucinations in LLM outputs, from uncertainty signals to retrieval-based verification.
A practical DSPy tutorial: build modular LLM programs with signatures, teleprompters, and RAG—plus evaluation and production tips.
A practical, end-to-end tutorial on instruction tuning LLMs: data design, LoRA/QLoRA training code, evaluation, deployment, and production tips.
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 clear, practical guide to RLHF—how human preferences train models, the pipeline, pitfalls, and modern variants like DPO and RLAIF.
