This comprehensive Generative AI for Developers course provides a complete hands-on roadmap for building modern AI applications using Large Language Models (LLMs), vector databases, and advanced AI engineering tools. It is designed for developers, AI engineers, and technical learners who want practical experience with real-world AI systems.

The course starts with the fundamentals of generative AI, including AI pipelines, data preprocessing, vectorization, and text classification. It then introduces Large Language Models and explains transformer architecture, attention mechanisms, and how systems like ChatGPT are trained.

You will gain hands-on experience with Hugging Face tools, including transformers, datasets, tokenization, feature extraction, and fine-tuning pretrained models. The course also includes practical projects such as text summarization, text-to-image generation, and text-to-speech applications.

A major section focuses on OpenAI APIs, including ChatCompletion APIs, function calling, GPT fine-tuning, Whisper audio transcription, and DALL·E image generation. Learners also build real applications like Telegram bots powered by AI.

The course dives deeply into vector databases including ChromaDB, Pinecone, and Weaviate, which are essential for Retrieval-Augmented Generation (RAG) systems. You will also master LangChain features such as prompt templates, chains, agents, memory systems, and document loaders.

Advanced topics include open-source LLMs like Llama, Falcon, and Mistral, along with LoRA and QLoRA fine-tuning methods. The course also teaches LlamaIndex, AI deployment pipelines, LLMOps, Vertex AI, AWS Bedrock, and production-grade RAG applications.

By the end of this course, you will be able to build, fine-tune, deploy, and manage enterprise-level generative AI applications using the latest AI frameworks and cloud platforms.