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Generative AI for Developers

Course Summary

This course introduces participants to the fundamentals of working with Large Language Models (LLMs) using the OpenAI API. Participants will learn how to structure and refine prompts, build a simple chatbot using Python, and apply best practices for responsible AI usage and cost-effective integration into development workflows.

Prerequisites:

To get the most out of the session, participants should have:

  • Experience programming with Python
  • Familiarity with using external libraries and working with APIs

Purpose
Understand the fundamentals of Large Language Models (LLM) using the OpenAI API
Audience
Professionals who want to leverage LLMs for application development utilizing AI-powered features
Role
Software Developers | Data Scientists | Solution Architects
Skill level
Intermediate
Style
Lecture | Hands-on Activities | Labs
Duration
2 days
Related technologies
AI/ML | Gen AI | Python | OpenAI

 

Course objectives
  • Explain how Large Language Models (LLMs) can support software development workflows
  • Write and iterate effective prompts using the OpenAI API
  • Build a basic chatbot that handles multi-turn conversations
  • Apply responsible development and cost optimization practices with LLMs

What you'll learn:

In this Generative AI for Developers course, you'll learn:

Working with Large Language Models

  • LLM capabilities and limitations
  • OpenAI models overview (chat and completion APIs)
  • Understanding tokens, temperature, and system roles

Prompt structure: clarity, format, constraints

  • Prompt Engineering Techniques
  • Designing prompts for different output types
  • Iterating on prompts for improved performance
  • Maintaining context in multi-turn interactions
  • Leveraging API parameters effectively

Building an AI-Powered Chatbot

  • Defining chatbot goals and use cases
  • Creating conversation flows
  • Implementing context handling and simple memory
  • Using OpenAI’s chat API in a Python application

Best Practices for Responsible AI Use

  • Cost management (token limits, rate limits, caching)
  • Handling errors and bad input
  • Mitigating bias and harmful responses
  • Ethical considerations and content moderation

Generative AI for Developers

real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
fully customized

Tailored Outlines

Once-size-fits-all doesn't apply to training teams. That's where we come in!

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