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Building Applications with OpenAI API

Course Summary

The Building Applications with OpenAI API course will provide participants with the knowledge to create effective prompts, manage context in conversations, integrate a dynamic knowledge base, and implement Retrieval-Augmented Generation (RAG) for more relevant responses.


Prerequisites
In order to succeed in this course, you will need:

  • Intermediate Python programming skills
  • Basic Understanding of RESTful APIs
  • Foundational Knowledge of Machine Learning or AI Concepts
  • Client must provide access to an OpenAI API Keys for participants

Purpose
Learn how to build effective prompts, add context to API calls, and implement using Retrieval-Augmented Generation (RAG)
Audience
Anyone wishing to gain an understanding of building effective applications using OpenAI APIs
Role
Software Engineers | Software Developers | Technology Managers
Skill level
Intermediate
Style
Lectures | Hands-on Activities | Labs | Mini Project
Duration
3 days
Related technologies
Gen AI | Python | RESTful APIs |Machine Learning | AI

 

Course objectives
  • Interact with OpenAI's API to make basic and advanced calls
  • Understand the structure of responses, and manage API usage
  • Design and optimize prompts for a variety of use cases
  • Manage conversation context to build engaging interactions in applications
  • Create a simple knowledge base using Python
  • Implement Retrieval-Augmented Generation (RAG)

What you'll learn:

In this Building Applications with OpenAI API course, you'll learn:

Introduction to OpenAI API & Building Effective Prompts

  • Introduction to OpenAI API
    • Overview
    • API setup
    • Reading API responses
    • Understanding tokens and usage limits
  • Crafting Effective Prompts
    • Best practices
    • Adjusting parameters

Context Management & Building a Knowledge Base

  • Adding Context to API Calls
    • Using conversation history
    • Managing token limits
  • Introduction to Knowledge Bases
    • Creating a simple knowledge base using JSON

Implementing Retrieval-Augmented Generation (RAG)

  • Understanding RAG
    • What is RAG?
    • Enhancing AI capabilities
    • Introduction to vector embeddings and semantic search
  • Using Vector Databases
    • Setting up a vector database for quick retrieval

Mini Project: Building and Refining an Application that uses RAG

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