Learn Generative AI

Understand what Generative AI is, how it differs from traditional AI, and why it's revolutionizing every industry.

Trace the journey from early AI research to modern Generative AI, including key breakthroughs and milestones.

Understand the architecture and mechanisms behind LLMs — transformers, attention, tokenization, and text generation.

Essential vocabulary and concepts for working with Generative AI — tokens, prompts, parameters, APIs, and more.

Learn the foundations of prompt engineering — how to write clear, effective prompts that get the best results from LLMs.

Master advanced prompting strategies — chain-of-thought, few-shot, role prompting, self-consistency, and more.

Learn reusable prompt patterns and frameworks for common tasks — templates you can adapt for any GenAI use case.

Learn systematic methods to evaluate prompt effectiveness, measure output quality, and iterate toward better results.

Learn how to set up and use the OpenAI API to build AI-powered applications with GPT models.

Learn how to use the Anthropic API to build applications with Claude — featuring long context, safety, and advanced reasoning.

Learn how to use Google's Gemini API for text, vision, and multimodal AI applications.

Compare OpenAI, Anthropic, and Google — models, pricing, features, and when to use each for your AI applications.

Learn how to build intelligent chatbots with conversation memory, persona management, and real-time streaming.

Build applications for content generation, summarization, translation, and text transformation using LLMs.

Build AI-powered code generation tools — from autocomplete to full project scaffolding using LLMs.

Build applications that combine text, images, audio, and video using multimodal LLMs.

Understand Retrieval-Augmented Generation (RAG) — the technique that connects LLMs to your own data for accurate, grounded responses.

Deep dive into vector databases and embedding models — the backbone of RAG and semantic search systems.

Build complete RAG pipelines using LangChain and LlamaIndex — from document loading to production-ready Q&A systems.

Improve RAG performance with hybrid search, reranking, query transformation, and other advanced retrieval strategies.

Learn when and how to fine-tune LLMs — customizing models for specific tasks, domains, and output styles.

Learn LoRA and other efficient fine-tuning methods for open-source LLMs — train custom models without massive GPU requirements.

Learn how to prepare high-quality training datasets and evaluate fine-tuned model performance systematically.

Understand AI agents — autonomous systems that reason, plan, and take actions using LLMs and tools.

Master function calling and tool use — the mechanism that lets LLMs interact with external APIs, databases, and systems.

Build autonomous AI agents that plan, execute, and adapt — from research assistants to code generators.

Build systems where multiple specialized AI agents collaborate — from team coordination to complex workflow automation.

Learn how to deploy AI applications to production — APIs, scaling, monitoring, caching, and cost optimization.

Understand AI safety principles, bias mitigation, prompt injection defense, and building responsible AI systems.

Explore what's next for Generative AI — emerging trends, technologies, and opportunities shaping the future of AI.