Notes tagged with

ai-agents

A guide to deciphering model names for better AI Engineering decisions

Learn how to effectively control the behavior of Large Language Models (LLMs) using inference parameters like temperature, top-p, and more. This guide provides practical examples with Python and LangChain.

Learn how to create precise, structured prompts for AI agents using the CO-STAR framework, enhancing reliability and performance in LLM applications.

Exploring the StateAct pattern to enhance AI agents' robustness and long-term task management. Learn how to implement this pattern using LangGraph and Ollama, ensuring agents maintain focus and clarity in complex tasks.

From solo problem-solvers to orchestrated teams. This guide explores the 'why' and 'how' of multi-agent architectures, demonstrating how a team of specialized AI agents can solve complex problems more effectively than a single agent ever could.