Tool Use & Function Calling Patterns for LLM Apps
Design reliable tool calls, schema contracts, and safe execution loops.
Tool Use & Function Calling Patterns for LLM Apps Tool use is how you turn a language model into a useful product. Instead of only generating text, the model can call tools: search, databases, calculators, APIs, or internal systems. This tutorial gives you practical patterns to make tool use reliable and safe. Define Clear Tool Contracts Your tool schema is your API. Keep it strict and explicit. The model cannot call a tool correctly if the schema is vague. Use a Small Allowlist Only expose tools you are ready to support. Every tool is another surface area for errors and abuse. Start with 2-3 tools and expand when you have monitoring in place. Validate Before You Execute Never trust model output blindly. Validate tool arguments with real runtime checks. Validation turns unpredictable output into predictable behavior. Keep Tool Results Small and Clean If the tool returns huge data, the model will choke. Summarize results or cap size before returning to the model. A good rule: only return what you want the model to use. Use a Two-Step Loop for Safety Model decides which tool to call and with what args. Your system executes the tool and returns results. Never let the model execute tools directly. Keep it in a controlled loop. Make Tools Idempotent When Possible If a tool can be called twice safely, your system is more resilient. For example, a "getUser" tool is safer than a "chargeCard" tool. For side-effect tools, add human confirmation or stricter validation. Handle Errors as First-Class Outputs Tool failures are normal: timeouts, missing data, permissions. Handle them gracefully and pass clear error messages to the model. Then instruct the model to explain the error...
Tags: Generative AI, Tools, Function Calling