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Welcome to this week’s edition of AI Distilled! As always, we’re bringing you the most relevant breakthroughs, product launches, expert insights, and grassroots meetups exploring next-gen techniques.

Here’s what’s new this week.

LLM Expert Insights,

Packt

1. The Test Tribe Houston – RAG Meetup
Date: July 17, 2025
Location: Houston, TX, USA
Cost: TBA
Focus: RAG with LLMs, coherence improvements using RAGAS

2. Utah Java Users Group – GenAI Meetup
Date: July 17, 2025
Location: South Jordan, UT, USA
Cost: Free
Focus: Hands-on implementation of RAG in production environments

3. Agentic RAG – Online Meetup
Date: July 19, 2025
Location: Online
Cost: Free
Focus: Applying agentic systems to Retrieval-Augmented Generation

4. PyData Milton Keynes – RAG Applications

Date: July 17, 2025
Location: Milton Keynes, UK
Cost: Free
Focus: RAG in Python using Hugging Face and LangChain

5. IR-RAG @ SIGIR 2025
Date: July 13–18, 2025
Location: Padua, Italy
Cost: TBA
Focus: Information retrieval’s evolving role in Retrieval-Augmented Generation

What’s stopping you? Choose your city, RSVP early, and step into a room where AI conversations spark, and the future unfolds one meetup at a time.

Dashboards alone aren’t cutting it. The market’s moving toward something new: data apps, live collaboration, and AI that works the way teams actually work.

See what's driving the rise of Next Gen BI, how Sigma earned a top debut on the Gartner Magic Quadrant, and what’s next for our roadmap.

Incorporating function-calling capabilities into intelligent agents has emerged as a transformative practice in recent AI development. This guide by our experts Ajanava Biswas and Wrick Talukdar explores how OpenAI tools can be employed to create agentic systems that perform real-world tasks by calling external functions based on user inputs. This integration enables agents not only to understand intent but also to take contextual actions with structured logic.

Let’s get started.

What Is Function Calling in LLMs?
Function calling allows large language models (LLMs) to invoke predefined functions using structured input provided by the user. It bridges the gap between conversational input and executable system logic, enhancing the agent’s ability to act upon user requests.

Let’s take an example of a travel booking agent that uses the function calling feature to book a flight. The LLM decides when to invoke the function based on the user's message and then provides the necessary arguments, such as departure city, arrival city, and travel date.

Let’s see how it works.

Setting up the function call: OpenAI’s Python SDK is used to define and invoke a function. Here is a minimal example of how to structure this process:

```python
import openai

def book_flight(passenger_name: str, from_city: str, to_city: str, travel_date: str) -> str:
return "A flight has been booked"

tools = [
{
"type": "function",
"function": {
"name": "book_flight",
...
}
}
]
```

2. Using the function in a conversation: The agent must decide when to call the function during a user interaction. Here's how the OpenAI API helps:

```python
response = openai.chat.completions.create(
model="gpt-4-turbo",
messages=[{"role": "user", "content": "Book a flight from LA to NY on Oct 1"}],
tools=tools
)
```

Upon detecting intent, the model populates the function arguments and issues a function call. If the user's intent is unclear or incomplete, the model may request additional information.

3. End-to-end interaction: Once the function is called, the result is returned to the model, which completes the dialogue:

Let's see this in action:

```python
response = openai.chat.completions.create(
model="gpt-4-turbo",
messages=[...], # includes user and function call messages
tool_choice="book_flight"
)
```

4. Sample conversation: The flow of a conversation may look like this:

```
User: I want to book a flight
Agent: Sure! I need some details: departure city, arrival city, date?
User: From LA to NY on Oct 1, my name is John Doe.
Agent: Great! Booking your flight now.
```

This conversational structure illustrates how seamlessly an LLM can gather information, invoke a function, and respond.

When you enable external function calls, intelligent agents are transformed from passive responders into proactive performers. This is foundational for building agentic systems that can interact with APIs, databases, or robotic control interfaces.

Create intelligent, autonomous AI agents that can reason, plan, and adapt

Whether it's a scrappy prototype or a production-grade agent, we want to hear how you're putting generative AI to work. Drop us your story at nimishad@packtpub.com or reply to this email, and you could get featured in an upcoming issue of AI_Distilled.

📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.

If you have any comments or feedback, just reply back to this email.

Thanks for reading and have a great day!

That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️

We would love to know what you thought—your feedback helps us keep leveling up.

Thanks for reading,

The AI_Distilled Team
(Curated by humans. Powered by curiosity.)