Generative AI Developer Guide: OpenAI, Gemini, and DeepSeek APIs

Integrating artificial intelligence into your software applications is no longer a luxury—it is a core requirement for modern development. This text-based course guides you through the practical steps of connecting to and leveraging major Large Language Model (LLM) APIs, including OpenAI, Gemini, and DeepSeek. You will transition from a beginner to a developer capable of building production-ready AI features. You will understand how to manage API keys securely, structure prompts for predictable outputs, implement Retrieval-Augmented Generation (RAG) to query private data, and fine-tune models for custom behaviors. What you'll learn: - Configure and authenticate connections to OpenAI, Gemini, and DeepSeek APIs using Python - Apply advanced prompt engineering techniques, including chain-of-thought prompting and structured JSON outputs - Build a retrieval-augmented generation (RAG) system using vector databases to query proprietary data - Implement conversational memory in chat applications to maintain context over multi-turn dialogues - Understand the fundamentals of fine-tuning models on custom datasets for specialized tasks - Manage API rate limits, error handling, and cost-optimization strategies for production deployment The course starts with fundamental concepts of generative AI and basic API setup before progressing to intermediate topics like semantic search, RAG architectures, and fine-tuning workflows. Through clear explanations and practical code examples, you will learn how to design robust AI-driven workflows. This course is designed for developers, software engineers, and tech-savvy professionals who want to integrate AI capabilities into their projects. No prior experience with artificial intelligence is required, though a basic understanding of Python is helpful. Start building your own intelligent, LLM-powered applications today.