1582 articles
A deep-dive into chain-of-thought prompting techniques including zero-shot CoT, few-shot CoT, self-consistency, and Tree of Thought — with Python code you can run today. Targeted at ML engineers and developers who need reliable LLM reasoning.
Learn how to use few-shot learning to teach LLMs new tasks with minimal examples, covering example selection, formatting, domain adaptation, and evaluation strategies. Targeted at AI engineers building classification, extraction, and generation pipelines.
A practical guide to writing system prompts that reliably shape LLM behavior, covering structure, role assignment, output format control, guardrails, and testing strategies. Designed for developers building production AI applications.
A comprehensive guide to building autonomous LLM agents with tool use, covering the ReAct pattern, function calling, memory systems, and multi-agent coordination — with production-ready Python examples. For ML engineers and backend developers building agentic AI systems.
A comprehensive guide to Microsoft AutoGen for building multi-agent AI systems, covering agent setup, GroupChat, code execution, custom tools, and production deployment patterns. For ML engineers building collaborative agent pipelines.