The world is yours

AI Application Development

A Practical Guide using OpenAI LLM

Enroll Back

Training Objectives

This program is designed to equip participants with the practical skills and knowledge to build functional applications powered by large language models. The core objective is to move from theoretical understanding to hands-on implementation, using the OpenAI API as the primary tool. Participants will learn to integrate LLM capabilities into software solutions, manage API interactions effectively, and apply best practices for prompt engineering and application design.

Target Participants

This training is ideal for software developers, engineers, product managers, and tech-savvy professionals who have a foundational understanding of programming (preferably in Python or JavaScript) and wish to add generative AI capabilities to their skill set. It is suitable for both individuals looking to innovate within their current roles and teams aiming to prototype or deploy AI-enhanced features.

Standard Modules to Be Covered

  • Module 1: Foundations of LLMs & The OpenAI Ecosystem - Overview of how LLMs work, key concepts (tokens, context windows), and navigating OpenAI's platform, models, and pricing.
  • Module 2: API Fundamentals & Authentication - Hands-on setup, making first API calls (Chat Completions, Completions), and managing API keys and security.
  • Module 3: Mastering Prompt Engineering - Techniques for crafting effective system and user prompts, using few-shot learning, and controlling output format and creativity.
  • Module 4: Building Context-Aware Applications - Implementing conversation memory, managing chat history, and utilizing embeddings for custom knowledge bases.
  • Module 5: Application Patterns & Integration - Architecting common AI app patterns (chatbots, content generators, classifiers) and integrating the API into web frameworks and backend services.
  • Module 6: Responsible Development & Optimization - Implementing safety best practices, cost management strategies, and performance monitoring for production applications.

Additional Materials

Participants will receive a comprehensive resource package including: a digital handbook with code snippets and architecture diagrams, access to a private GitHub repository with starter templates and example projects, a curated list of advanced reading and documentation links, and a set of prompt design cheat sheets for quick reference during development.

Training Methods

The training employs a hands-on, project-based learning approach. Instruction will blend concise lectures and live demonstrations with extensive guided coding labs. Participants will work on incremental exercises that culminate in a capstone project, such as building a custom chatbot or a document analysis tool. Real-time Q&A, code reviews, and collaborative problem-solving sessions will be integral to the methodology.

Expected Outcomes

Upon completion, participants will be able to: autonomously design and prototype applications leveraging the OpenAI API; write robust, effective prompts for diverse use cases; implement secure and cost-efficient API integrations; and make informed architectural decisions for scaling and maintaining AI-powered features. The goal is for each participant to finish with a working prototype and the confidence to deploy AI solutions in a professional context.

Enroll Back

What you will learn?

Introduction to AI and OpenAI LLMs

  • 1. The Rise of Generative AI

  • 2. Understanding Large Language Models (LLMs)

  • 3. Introducing OpenAI and Its Model Ecosystem

  • 4. Key Concepts: Tokens, Prompts, and Context Windows

  • 5. Setting Realistic Expectations and Identifying Use Cases

Setting Up Your Development Environment

  • 1. Creating an OpenAI Platform Account and Managing API Keys

  • 2. Understanding API Costs, Rate Limits, and Billing

  • 3. Choosing Your Development Language and SDK (Python/Node.js)

  • 4. Installing and Configuring the OpenAI SDK

  • 5. Essential Tools: Version Control, Environment Variables, and Testing Frameworks

Mastering the Art of Prompt Engineering

  • 1. Principles of Effective Prompt Design

  • 2. Working with System, User, and Assistant Roles

  • 3. Techniques: Zero-Shot, Few-Shot, and Chain-of-Thought Prompting

  • 4. Controlling Output: Temperature, Top-p, and Max Tokens

  • 5. Iterative Prompt Development and A/B Testing

Building Your First AI-Powered Application

  • 1. Designing a Simple Chat Interface

  • 2. Making Your First API Call to the Chat Completions Endpoint

  • 3. Handling Streaming Responses for Real-Time Interaction

  • 4. Implementing Conversation History and Memory

  • 5. Basic Error Handling and User Feedback

Working with Different OpenAI Models

  • 1. Comparing GPT-4, GPT-3.5, and Specialized Models

  • 2. Utilizing the Assistants API for Persistent Threads and Tools

  • 3. Generating and Editing Images with DALL-E

  • 4. Converting Speech to Text with the Whisper API

  • 5. Creating Embeddings for Search, Clustering, and Recommendations

Implementing Advanced Features and Patterns

  • 1. Building Function Calling for Structured Data Extraction

  • 2. Implementing Retrieval-Augmented Generation (RAG) with External Data

  • 3. Designing Multi-Step AI Workflows and Agents

  • 4. Integrating Moderation and Content Filtering

  • 5. Implementing Caching and Cost Optimization Strategies

Application Architecture and Production Readiness

  • 1. Designing Scalable and Secure Backend Services

  • 2. Managing State, Sessions, and Data Privacy

  • 3. Implementing Robust Logging, Monitoring, and Analytics

  • 4. Strategies for Latency Reduction and Performance Optimization

  • 5. Planning for Deployment: Containers, Serverless, and APIs

Responsible AI Development and Best Practices

  • 1. Understanding AI Bias, Fairness, and Ethical Considerations

  • 2. Ensuring Safety, Reliability, and Hallucination Mitigation

  • 3. Developing Inclusive and Accessible AI Applications

  • 4. Legal and Compliance Aspects: Terms of Use and Data Governance

  • 5. Staying Updated: Following AI Trends and Model Updates

From Prototype to Product: Case Studies and Project

  • 1. Case Study: Building an Intelligent Customer Support Bot

  • 2. Case Study: Creating a Content Summarization and Analysis Tool

  • 3. Case Study: Developing a Code Assistant and Documentation Generator

  • 4. End-to-End Project Plan: Ideation to Deployment

  • 5. Gathering User Feedback and Planning Iterative Improvements

Enroll Back