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.
A practical guide to multi‑turn conversational AI: architecture, memory, grounding, safety, and evaluation patterns for reliable, scalable assistants.
A practical, end-to-end guide to RAG evaluation metrics—from retrieval and grounding to faithfulness, relevance, and online impact.
A step-by-step guide to preparing high-quality datasets for LLM fine-tuning, from sourcing and cleaning to formats, safety, splits, and evaluation.
A practical guide to function calling vs. tool use in LLMs: architectures, trade-offs, design patterns, reliability, security, and evaluation.
