Join us for a hands-on deep dive into how to work with **LLM APIs**—the essential building blocks for integrating language models into your own applications.

Whether you’re using a cloud-based service like **OpenAI** or a local model via **Ollama**, understanding how APIs work will give you the power to move from experimenting to building.

### **📋**

### **Agenda**

**1\. What Is an LLM API?**

* Understand the anatomy of a typical API call to a language model
* Explore key components: endpoints, headers, payloads, and responses
* Learn the differences between chat and completion-style APIs

**2\. Calling a Proprietary API \(OpenAI\, Anthropic\)**

* Conceptually map out how to call a cloud-hosted LLM
* Review basic authentication and rate limits
* Consider privacy, cost, and latency trade-offs

**3\. Calling a Local LLM \(Ollama\)**

* See how local models expose similar APIs
* Explore advantages of running models on your own machine

**4\. Comparison & Use Cases**

* When should you use cloud vs. local APIs?
* Examples of small projects that combine both

Generative AI for Absolute Beginners

Alfred Essa

Erran Su

pratap

Bren

Darryl

Prem Ramamurthy

Michelle Gross

Thomas O'Dea

Alexandra Daniell

Jim R

Camilo Garzon

John White

Madhu Ramachandran

michael971

Jazmin Logrono

Vincent Nguyen

Kevin Hathaway

Danish

Chad Vawter

LLM API's Deep Dive

Online event

In this workshop, we’ll **write real code** to interact with both **proprietary LLMs** (like OpenAI and Anthropic) and **open-source local models** (like those run via Ollama).

This is your chance to move from conceptual understanding to building working prototypes using Python and REST APIs.

**1\. Coding a Proprietary LLM API \(OpenAI / Anthropic\)**

* Set up API keys securely
* Write Python code to send a prompt and receive a response
* Handle parameters (temperature, max tokens, etc.)

**2\. Coding a Local LLM API with Ollama**

* Start an Ollama server and test locally
* Use Python (requests or similar) to send prompts
* Compare performance and output with proprietary APIs

**3\. Bonus: Wrapping It in a Simple App**

* Create a basic CLI or web interface (e.g., Streamlit or Flask)
* Think about extending the code for your own projects

Vibe Code AI for Absolute Beginners

Phil

Kiran 

Harshal

Ryan Childress

Jeff

Len DeCarmine

Jim Bergh

Think

Darryl L

sravan kumar kanugula

Coding LLM APIs — From Cloud to Local

Machine Learning for Absolute Beginners

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Machine Learning for Absolute Beginners  is a learning community. Our goal is to help members develop practical skills in Artificial Intelligence and Machine Learning.



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Machine Learning for Absolute Beginners

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