A practical tutorial on reasoning models and chain-of-thought: safe prompting, self-consistency, tree-of-thought, tooling, and evaluation patterns.
Build a production-ready LangGraph multi-agent workflow with a supervisor, tools, checkpointing, and streaming—step-by-step with tested Python code.
A practical, end-to-end guide to reducing AI hallucinations with data, training, retrieval, decoding, and verification techniques.
Step-by-step guide to fine-tuning LLMs with LoRA/QLoRA using Transformers, PEFT, and TRL—from data prep to deployment.
Compare small and large language models across cost, latency, privacy, and accuracy. Includes routing patterns, tuning options, and a decision checklist.
A 2026 field guide to modern LLM prompt engineering: patterns, multimodal tips, structured outputs, RAG, agents, security, and evaluation.
