Developer Guide

Function Calling with Gemini 1.5 Pro

A practical guide to Gemini-style function calling: define structured actions, validate payloads, and connect model decisions to real systems.

Audience: Developers and technical operators wiring LLMs into CRMs, inboxes, spreadsheets, and internal APIs.

Why function calling matters

Text-only prompting is not enough when the workflow has to send an email, write to a CRM, or query a record. Function calling gives the model a constrained menu of actions so it can decide what to do without inventing its own format.

• Define a narrow tool contract for each action.
• Validate arguments before execution.
• Log model intent and tool output for debugging.

Python integration pattern

The most stable pattern is model decides, Python validates, then the business system executes. That keeps the model in the reasoning layer and keeps side effects inside normal application code.

• Schema first: write the tool signature before prompt design.
• Guard every tool call with validation and retries.
• Return machine-readable results back into the conversation loop.

What the course focuses on

Inside the curriculum, function calling is treated as operations engineering, not novelty. The emphasis is on dependable automations that survive bad inputs, ambiguous tickets, and edge-case user requests.

• Inbox triage and routing tools
• Lead qualification workflows
• Action logs, human review, and rollback rules