OpenAI API Development in Martinique

Module 1: Introduction to OpenAI API Development

• Overview of OpenAI and Generative AI applications.
• Understanding Large Language Models (LLMs) and their capabilities.
• Introduction to OpenAI API architecture and application use cases.
• Exploring AI-powered software development opportunities in Martinique.

Module 2: API Fundamentals and Environment Setup

• Understanding REST APIs, HTTP methods, requests, and responses.
• Setting up the Python development environment.
• Installing required libraries and dependencies.
• Configuring API credentials and environment variables securely.
• Understanding API authentication and request structures.

Module 3: OpenAI API Integration with Python

• Connecting Python applications to OpenAI APIs.
• Sending API requests and processing responses.
• Handling JSON data and structured response formats.
• Managing exceptions, connection errors, and API failures.
• Implementing reusable functions for API communication.

Module 4: Working with Language Models

• Understanding supported OpenAI model capabilities and use cases.
• Sending text inputs and generating AI responses.
• Configuring model parameters and output settings.
• Managing token usage, input limits, and response length.
• Evaluating model outputs for relevance and quality.

Module 5: Prompt Engineering and Message Design

• Understanding the principles of effective prompt design.
• Creating prompts with clear instructions, context, and constraints.
• Structuring messages for conversational applications.
• Applying examples and output-format instructions.
• Testing and refining prompts to improve response quality.

Module 6: Building Conversational AI Applications

• Developing multi-turn conversational workflows.
• Managing conversation history and contextual information.
• Creating chatbot interfaces using Python.
• Handling user inputs and generating context-aware responses.
• Implementing conversation flow and fallback responses.

Module 7: Structured Outputs and Data Extraction

• Generating structured information from unstructured text.
• Working with JSON outputs and supported structured-output features.
• Extracting entities, classifications, and key information.
• Validating generated data against application requirements.
• Integrating structured responses into downstream workflows.

Module 8: Tool Calling and Function Integration

• Understanding tool calling and function-based workflows.
• Defining tools and their input parameters.
• Connecting model responses to application functions.
• Processing tool results and returning relevant information.
• Building applications that combine AI responses with external functionality.

Module 9: Embeddings and Retrieval-Based Applications

• Understanding embeddings and semantic similarity.
• Exploring vector representations and semantic search concepts.
• Preparing documents and knowledge sources for retrieval.
• Connecting retrieved information with model prompts.
• Understanding the fundamentals of Retrieval-Augmented Generation (RAG).

Module 10: AI Application Development and Integration

• Building reusable AI application components.
• Integrating OpenAI APIs with databases, files, and external services.
• Developing text summarisation and information-processing workflows.
• Creating content-generation and document-assistance applications.
• Connecting AI functionality with existing software systems.

Module 11: Error Handling, Security, and Cost Management

• Handling API errors, timeouts, and rate limits.
• Implementing suitable retry and exception-handling strategies.
• Managing API keys and sensitive credentials securely.
• Monitoring token consumption and API usage costs.
• Applying data privacy, input validation, and responsible AI practices.

Module 12: Testing, Evaluation, and Deployment

• Testing API integration and application functionality.
• Evaluating AI responses for accuracy, consistency, and relevance.
• Debugging application issues and refining prompts.
• Implementing logging and performance monitoring.
• Understanding deployment considerations for AI-powered applications.

Module 13: Hands-On Capstone Project

• Designing an AI-powered application using OpenAI APIs.
• Implementing API integration and prompt engineering.
• Developing response handling, validation, and error management.
• Testing application workflows with realistic scenarios.
• Documenting the solution, evaluating results, and presenting the completed project.