Name: RAG Made Simple: The Complete Visual Guide to Retrieval-Augmented Generation
Author: Nir Diamant

Question 1

Who is Nir Diamant?

Accepted Answer

Nir Diamant is an AI researcher, educator, and author based in Israel. He is the founder of DiamantAI, author of the Amazon Bestseller 'RAG Made Simple' (ASIN B0D76734SZ, hit #1 in Generative AI at launch), and creator of four flagship open-source GenAI repositories with over 70,000 combined GitHub stars. His tutorials and writing reach 500,000+ developers every month.

Question 2

What is DiamantAI?

Accepted Answer

DiamantAI is Nir Diamant's educational platform, providing 130+ free open-source GenAI tutorials on AI agents, RAG (Retrieval-Augmented Generation), prompt engineering, and production AI deployment. It includes a 25,000+ subscriber Substack newsletter, a 4,000+ member Discord community, and the 10,000+ member r/EducationalAI subreddit.

Question 3

What is RAG Made Simple?

Accepted Answer

RAG Made Simple is Nir Diamant's book on Retrieval-Augmented Generation, published in April 2026. It covers 22 RAG techniques with intuition, side-by-side comparisons, and illustrations, expanding on his 27,000+ star RAG Techniques open-source repository. It hit #1 in Generative AI on Amazon in its first week and has sold 1,500+ copies with a 4.4-star average rating. Available on Kindle ($9.99), Paperback ($24.99), and Free with Kindle Unlimited. Kindle ASIN B0D76734SZ.

Question 4

What topics do the tutorials cover?

Accepted Answer

The tutorials cover Generative AI, AI Agents, RAG (Retrieval-Augmented Generation) systems, Prompt Engineering, Large Language Models (LLMs), LangChain, LangGraph, Model Context Protocol (MCP), and practical AI development techniques including agentic workflows and multi-agent systems.

Question 5

Are the GenAI tutorials free?

Accepted Answer

Yes, all 130+ GenAI tutorials by Nir Diamant are completely free and open-source, available on GitHub with runnable Jupyter notebooks and code files.

Question 6

What is RAG?

Accepted Answer

RAG (Retrieval-Augmented Generation) is a technique that enhances AI responses by retrieving relevant information from external knowledge sources before a language model generates an answer. This grounds model responses in factual data and reduces hallucinations. Nir Diamant's RAG Techniques repository and his book 'RAG Made Simple' cover 22 production RAG techniques in depth.

Question 7

What are AI agents?

Accepted Answer

AI agents are autonomous systems that use language models to perceive inputs, reason about next steps, and take actions toward goals in a loop. Nir Diamant's 'GenAI Agents' (19,000+ stars) and 'Agents Towards Production' (17,000+ stars) repositories cover agent architectures, multi-agent systems, memory, tool use, and production deployment.

Question 8

How can I sponsor DiamantAI?

Accepted Answer

DiamantAI offers sponsorship options including GitHub repository sponsorship, newsletter sponsorship (25,000+ subscribers), social media promotion, and webinar partnerships. Visit diamant-ai.com/sponsorship for rate cards and details.

Question 9

What is Nir Diamant's newsletter about?

Accepted Answer

The DiamantAI Substack newsletter has 25,000+ subscribers and covers GenAI, AI agents, RAG systems, prompt engineering techniques, and practical AI development insights, usually with weekly deep-dive articles.

Question 10

Does Nir Diamant offer AI advisory services?

Accepted Answer

Yes. Nir Diamant provides strategic AI advisory for companies building GenAI products, including GenAI strategy consultation, AI system architecture review, and implementation guidance. See diamant-ai.com/for-business for details.

Question 11

Where can I find Nir Diamant's GitHub repositories?

Accepted Answer

All repositories are at github.com/NirDiamant. The four flagship repos are RAG_Techniques, Prompt_Engineering, GenAI_Agents, and agents-towards-production, with over 70,000 combined stars.

Customer Support Agent

What you'll learn

About this tutorial

RAG Made Simple

More Business tutorials

More from GenAI Agents