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.
Design a reliable AI summarization API for news: architecture, schema, grounding, evaluation, safety, compliance, and cost strategies.
A practical tutorial on reasoning models and chain-of-thought: safe prompting, self-consistency, tree-of-thought, tooling, and evaluation patterns.
A practical, end-to-end guide to reducing AI hallucinations with data, training, retrieval, decoding, and verification techniques.
A practical guide to the ReAct (Reason + Act) pattern for agentic AI, with design choices, code, safety, and evaluation tips.
