RAG, AI Agents and Generative AI with Python and OpenAI
Год выпуска: 2026
Производитель: Udemy
Сайт производителя:
https://www.udemy.com/course/generative-ai-rag/
Автор: Diogo Alves de Resende
Продолжительность: 38ч 30м
Тип раздаваемого материала: Видеоурок
Язык: Английский, субтитры- русский,датский,английский,французский,немецкий,корейский,португальский,испанский,турецкий,вьетнамский
Описание: 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.
About Your Instructor
Hi, I'm Diogo, a data expert with a Master's degree in Management specializing in Analytics from ESMT Berlin.
With extensive experience tackling complex business challenges—from managing billion-euro sales planning to conducting A/B tests that led to significant investments—I bring real-world expertise to this course.
As a startup founder helping restaurants worldwide optimize menus and pricing through data insights, I'm passionate about leveraging AI for practical solutions.
Personalized Support
One of the key benefits of this course is the direct access to me as your instructor.
I personally respond to all your questions within 24 hours.
No outsourced support—just personalized guidance to help you overcome challenges and advance your skills.
Continuous Improvements
I'm dedicated to keeping this course up-to-date with the latest advancements in AI.
Your feedback shapes the course—I'm always listening and ready to add new content that benefits your learning journey.
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.
Содержание
01-RAG_and_Generative_AI_with_Python
02-Python_for_RAG_and_AI
03-PART_A_INTRODUCTION_TO_RAG
04-Your_First_RAG_with_Flowise
05-Scientific_Literature_Review_RAG
06-Prompt_Engineering_System_Message
07-Scientific_Literature_LLMs
08-Prompt_Engineering_Temperature_and_Top_P
09-Prompt_Engineering_Techniques
10-Capstone_Project_RAG
11-Introduction_to_RAG_Practice_Test
12-Mid_Course_Feedback
13-PART_B_RAG_WITH_OPENAI_API
14-OpenAI_API
15-CAPSTONE_PROJECT_GenAI_for_Customer_Acquisition
16-RAG_with_OpenAI_File_Search
17-PART_C_RAG_WITH_UNSTRUCTURED_AND_MULTIMODAL_DATA
18-RAG_with_Unstructured_Data
19-Multimodal_RAG
20-CAPSTONE_PROJECT_Multimodal_Data
21-PART_D_ADVANCED_TOPICS_IN_RAG
22-Knowledge_Graph_with_LightRAG
23-Agentic_RAG_AI_Agents_for_RAG
24-RAGAS_Evaluating_RAG
25-PART_E_AI_AGENTS
26-AI_Agents_with_CrewAI
27-CAPSTONE_PROJECT_The_AI_Product_Manager_with_CrewAI
28-AI_Agents_with_OpenAI_Swarm
29-OPENAI_SWARM_CAPSTONE_PROJECT_The_Psychiatrist
30-PART_F_ADVANCED_TOPICS_IN_GENERATIVE_AI
31-Reasoning_Models
32-OpenAI_API_Image_Endpoint
33-Fine_Tuning_OpenAI_GPT_Models
34-MCP_with_OpenAI
35-End_of_Course_Feedback
36-APPENDIX_Python_Crash_Course
37-Python_Essentials
38-Book_Review
39-Variable_Types_and_Operators
40-If_else_and_Conditionals
41-Python_Intermediate
42-PYTHON_CAPSTONE_PROJECT_Virtual_Escape_Game
43-Introduction_to_Classes
44-PYTHON_CAPSTONE_PROJECT_Bitte_Eats
45-What_s_Next
Файлы примеров: присутствуют
Формат видео: MKV
Видео: AV1 1920x1080 16:9 30к/сек 400 кбит/сек
Аудио: Opus 48 кГц 64 кбит/сек 2 канала