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Welcome to the 101st edition of our newsletter!

This week, the world of AI is buzzing with significant developments. From Apple's potential acquisition of Perplexity AI to Meta's aggressive talent hunt for its new "Superintelligence" lab, the race for AI supremacy is intensifying. Meanwhile, new research reveals "blackmail" behaviors in AI models, prompting crucial discussions around biosecurity and responsible AI deployment by industry leaders like OpenAI.

Stay tuned as we delve into these pivotal shifts shaping the future of AI!

LLM Expert Insights,

Packt

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Date: October 10, 2025
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Date: February 19–22, 2025
Location: New York City, NY – AI Engineer Collective
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Focus: Engineering agent architectures, agent dev tools, and evaluation frameworks

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Date: September 29–30, 2025
Location: Herndon, VA – AI Agent Event
Cost: US $695 (Early Bird), $995 (Regular)
Focus: Enterprise agent systems, real-world agent deployment, decision-making frameworks

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Date: November 14, 2025
Location: San Francisco, CA – Global AI Community
Cost: Free to $99 (based on venue and track)
Focus: Building, deploying, and scaling autonomous agent

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Getting a model to generate text is easy. Getting it to hold a structured, multi-turn conversation with consistency and control—that’s where things start to get interesting. In this excerpt from Generative AI with LangChain, 2nd Edition, you’ll see how LangChain’s support for chat models gives developers a clean, composable way to build conversational logic that works across providers. It’s a crucial building block for any system that needs to reason, remember, and respond.

Working with chat models

Chat models are LLMs that are fine-tuned for multi-turn interaction between a model and a human. These days most LLMs are fine-tuned for multi-turn conversations. Instead of providing the model with an input such as

human: turn1

ai: answer1

human: turn2

ai: answer2

Model providers typically do not persist chat history on the server. Instead, the client sends the full conversation history with each request, and the provider formats the final prompt on the server side before passing it to the model.

SELECT line1, city, state, zip from

person p, person_address pa, address a

WHERE p.name = 'John Doe'

and pa.person_id = p.id

and pa.address_id = a.id

ORDER BY pa.start ASC

LIMIT 2, 1

LangChain follows the same pattern with ChatModels, processing conversations through structured messages with roles and content. Each message contains the following:

Role (who's speaking), which is defined by the message class (all messages inherit from BaseMessage)

Content (what's being said)

Key message types include:

Let's see this in action:

from langchain_anthropic import ChatAnthropic

from langchain_core.messages import SystemMessage, HumanMessage

chat = ChatAnthropic(model="claude-3-opus-20240229")

messages = [

SystemMessage(content="You're a helpful programming assistant"),

HumanMessage(content="Write a Python function to calculate factorial")

]

response = chat.invoke(messages)

print(response)

Here's a Python function that calculates the factorial of a given number:

```python

def factorial(n):

if n < 0:

raise ValueError("Factorial is not defined for negative numbers.")

elif n == 0:

return 1

else:

result = 1

for i in range(1, n + 1):

result *= i

return result

```

Let’s break this down. The factorial function is designed to take an integer n as input and calculate its factorial. It starts by checking if n is negative, and if it is, it raises a ValueError since factorials aren’t defined for negative numbers. If n is zero, the function returns 1, which makes sense because, by definition, the factorial of 0 is 1.

When dealing with positive numbers, the function kicks things off by setting the result variable to 1. Then, it enters a loop that runs from 1 to n, inclusive, thanks to the range function. During each step of the loop, it multiplies the result by the current number, gradually building up the factorial. Once the loop completes, the function returns the final calculated value. You can call this function by providing a non-negative integer as an argument. Here are a few examples:

```python

print(factorial(0)) # Output: 1

print(factorial(5)) # Output: 120

print(factorial(10)) # Output: 3628800

print(factorial(-5)) # Raises ValueError: Factorial is not defined for negative numbers.

```

Note that the factorial function grows very quickly, so calculating the factorial of large numbers may exceed the maximum representable value in Python. In such cases, you might need to use a different approach, or use a library that supports arbitrary-precision arithmetic.

Alternatively, we could have asked an OpenAI model such as GPT-4 or GPT-4o:

from langchain_openai.chat_models import ChatOpenAI

chat = ChatOpenAI(model_name='gpt-4o')

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Thanks for reading and have a great day!

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

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Thanks for reading,

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