How To Use LLMs: Tool Use/Function Call with OpenAI

Large Language Models (LLMs) can generate human-like text, but what if you want your LLM-powered app to do more than chat? Like extract structured data, trigger logic, or interact with databases/APIs?

Tool Use/Function Calling helps our LLMs do more than generate responses based on trained data.

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💡 What Is Tool Use/Function Call in LLMs?

Function call/Tool use is the pattern where the LLM decides when and how to invoke external capabilities (APIs, DB queries, search, calculators, code runtimes, and more) by returning a structured call.
Your application executes that call, returns the result to the model, and the model produces the final user response.

Why use it?

LLMs are smart, but they have limitations:

• They hallucinate
• They don’t fetch real-time data
• They can’t execute backend logic directly
• They return freeform text, which is sometimes hard to parse

Using tools solves this:

✅ Return structured outputs (like JSON)
✅ Fetch real-time information
✅ Integrate with APIs or your database
✅ Run backend logic (math, validation, scheduling, etc.)
✅ Trigger workflows or APIs

📌 A currency converter tool use example

1. Tool Definition
Define a function that converts currency:

const convert_currency = {
  "name": "convert_currency",
  "description": "Converts an amount from one currency to another",
  "parameters": {
    "type": "object",
    "properties": {
      "amount": { "type": "number" },
      "from": { "type": "string", "description": "Currency code, e.g., USD" },
      "to": { "type": "string", "description": "Currency code, e.g., EUR" }
    },
    "required": ["amount", "from", "to"]
  }
}

2. User Prompt

"How much is 100 dollars in euros?"

3. What Happens

• The LLM understands the request
• Calls the convert_currency tool with:

{
  "amount": 100,
  "from": "USD",
  "to": "EUR"
}

• Tool returns: 91.23 EUR
• LLM responds:

100 USD is approximately 91.23 EUR.

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📌 Function Calling With OpenAI: Job Description Analyzer

In Job Application Assistant, I used Function Calling to extract job insights.

The LLM pulls out from the job description:

• Required skills
• Responsibilities
• Experience or qualifications

Step 1: Define the Schema

const jobInsightFunction = {
  name: "extract_job_insights",
  description: "Extracts skills, responsibilities, and experience from a job description.",
  parameters: {
    type: "object",
    properties: {
      skills: {
        type: "array",
        items: { type: "string" },
        description: "List of skills required for the job",
      },
      responsibilities: {
        type: "array",
        items: { type: "string" },
        description: "Job responsibilities",
      },
      experience: {
        type: "array",
        items: { type: "string" },
        description: "Qualifications or experience needed",
      },
    },
    required: ["skills", "responsibilities", "experience"],
  },
};

Step 2: Call the Model with Tool

const response = await openai.chat.completions.create({
  model: "gpt-4-0613",
  messages: [
    { role: "system", content: "You are a helpful AI job assistant." },
    {
      role: "user",
      content: `Extract the key skills, responsibilities, and required experience from the following job description:\n\n${jobDescription}`,
    },
  ],
  tools: [
    {
      type: "function",
      function: jobInsightFunction,
    },
  ],
  tool_choice: "auto",
});

Step 3: Get and Use the Arguments

const toolCall = response.choices?.[0]?.message?.tool_calls?.[0];
const args = JSON.parse(toolCall?.function?.arguments ?? "{}");

Output:

args = {
  skills: [...],
  responsibilities: [...],
  experience: [...],
}

With this output, I can:
✅ Display in UI
✅ Match with resumes
✅ Generate cover letters

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📌 Quick Tips

• Use clear schema definitions
• Validate the output
• Use tool_choice: "auto" to let the model decide
• Chain tasks if needed: extract 👉🏼 reason 👉🏼 act

Happy coding!!!