Beyond Chatbots
The first wave of GenAI was dominated by chat interfaces. The next wave is about Agents—systems that can take action. To build effective agents, we need models that can speak the language of machines: structured data.
The Problem with Natural Language
Parsing a paragraph of text to extract a date or a price is fragile. It requires complex regex and is prone to errors if the model phrases things slightly differently.
Enter Function Calling
Function calling (or Tool Use) allows you to describe functions to the model, and have it output a JSON object containing the arguments to call those functions. This turns the LLM into a reasoning engine that drives your existing code.
Ensuring Type Safety
Even with function calling, models can hallucinate arguments or output invalid JSON. This is where schema validation guardrails come in. By enforcing a strict Zod or JSON Schema on the model's output, we can guarantee that our downstream code receives valid data.
Use Cases
- Data Extraction: Converting unstructured emails into database records.
- Workflow Automation: Agents that can query APIs, process data, and send reports.
- UI Generation: Generating dynamic UI components based on user intent.
Structured outputs are the bridge between the probabilistic world of AI and the deterministic world of software engineering.