RAG, AI Agents and Generative AI with Python and OpenAI 2026

ABOUT THE COURSE:

Unlock the Power of RAG, AI Agents, and Generative AI with Python and OpenAI in 2026!

Welcome to "RAG, AI Agents, and Generative AI with Python and OpenAI 2026"—the ultimate course to master Retrieval-Augmented Generation (RAG), AI Agents, and Generative AI using Python and OpenAI's cutting-edge technologies.

If you aspire to become a leader in artificial intelligence, machine learning, and natural language processing, this is the course you've been waiting for!

Why Choose This Course?

• Full-stack RAG: retrieval → augmentation → grounded generation with citations, sources, and guardrails.

• OpenAI-first: GPT-5, Responses Endpoint, File Search vector stores, image generation, Whisper, CLIP.

• No-code + code: Flowise visual pipelines and Python implementations (FAISS, LangChain, Streamlit).

• Evaluation-driven: RAGAS metrics (context precision/recall, response relevancy, factual correctness).

• Agentic systems: CrewAI and OpenAI Swarm for multi-agent orchestration, tools, memory, and state.

• Advanced GenAI: reasoning models (setup, prompting, verification), fine-tuning, MCP approvals, secure integrations.

• Business outcomes: customer support copilots, knowledge search, policy Q&A, analytics assistants, finance research, content operations.

Hands-on projects you actually ship

• No-code Flowise RAG (zero to answers with citations).

• OpenAI File Search RAG + Streamlit app (upload, index, chat).

• Unstructured data RAG (Excel/Word/PPT/EPUB/PDF).

• Multimodal RAG (Whisper + CLIP + cosine search).

• CrewAI & Swarm agent systems (researcher, writer, counselor, product manager).

• Reasoning model demos (setup, prompting, verification).

• Image generation pipelines (single/batch edits, animated GIFs).

• Fine-tuned GPT evaluation and testing.

What You'll Learn

• RAG architecture: retrieval, augmentation, grounded generation, source citations, metadata.

• Embeddings & vector stores: semantic search, nearest neighbors, FAISS, File Search.

• Chunking strategies: fixed/semantic/hierarchical, overlaps, LongRAG.

• System messages and prompt engineering: temperature, top-p, few-shot, persona.

• Reasoning models: chain-of-thought controls, verification, structured output.

• Agentic patterns: planning, tool use, memory/state, error handling.

• MCP with approvals: safe external actions (web fetch, APIs, Stripe).

• Evaluation with RAGAS: context precision/recall, relevancy, factual correctness.

• Deployment: Streamlit, environment secrets, requirements, debugging.

Why Master RAG and AI Agents Now?

The future of AI lies in systems that can retrieve relevant information and generate intelligent responses—Retrieval-Augmented Generation is at the forefront of this revolution.

By mastering RAG, AI agents, and generative models, you position yourself at the cutting edge of technology, making you invaluable in today's tech landscape.