tool use
6 results
Function Calling Explained: How AI Models Use Tools (With Real Examples)
Function calling lets LLMs request specific tool actions rather than just generating text. Here's how it works, when to use it, and practical examples in Python.
ReAct Prompting: Reasoning + Acting in a Loop
ReAct interleaves reasoning (Thought) and action (Act) steps so an AI agent can plan, use tools, and adjust its approach based on real-world feedback — all within a single prompt loop.
Program-Aided Language Models (PAL): Offload Computation to Code
PAL has an LLM write code to solve problems instead of computing answers directly — eliminating arithmetic errors and enabling complex calculations that pure language models consistently get wrong.
Function Calling: Giving LLMs Tools
Function calling is the technical mechanism that lets an LLM invoke external tools. Learn how to define tools, how models decide when to call them, and how to structure results so agents act reliably.
ReAct Prompting: Reason Before You Act
ReAct is the reasoning pattern that makes agents dramatically more reliable. By explicitly writing out thoughts before every action, the model plans better, catches errors earlier, and produces work you can follow and debug.
Agentic Prompting: Designing Prompts for AI Agents
AI agents don't just answer questions — they plan, use tools, and take multi-step actions. Learn how to design prompts that make autonomous AI systems reliable, safe, and effective.