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.

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.

What is RAG Made Simple?

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.

What topics do the tutorials cover?

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.

Are the GenAI tutorials free?

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

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.

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.

How can I sponsor DiamantAI?

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.

What is Nir Diamant's newsletter about?

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.

Does Nir Diamant offer AI advisory services?

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.

Where can I find Nir Diamant's GitHub repositories?

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.

Model Context Protocol (MCP) Explained

November 4, 2025byNir Diamant

The Model Context Protocol (MCP) is an open standard created by Anthropic that solves one of the biggest headaches in AI development: connecting language models to external tools and data sources. Before MCP, every AI application had to build custom integrations for every tool it wanted to use, creating an N×M problem where N applications each needed M separate integrations. MCP replaces this with a universal protocol where any MCP-compatible client can connect to any MCP server.

MCP uses a client-server architecture with three core primitives. Tools are functions the AI can call (like searching a database or sending an email). Resources are data the AI can read (like files or API responses). Prompts are reusable templates that help the AI use tools effectively. The protocol supports two transport mechanisms: stdio for local integrations (the MCP server runs as a subprocess) and HTTP with Server-Sent Events for remote integrations (the server runs on a different machine).

What makes MCP transformative is the ecosystem effect. Once someone builds an MCP server for a tool (say, GitHub or Slack), every MCP-compatible AI application can use it immediately, no additional integration work needed. This is why the protocol has seen rapid adoption across the AI ecosystem, with hundreds of open-source MCP servers already available. The protocol also includes important security features like capability negotiation, where clients and servers agree on what operations are allowed before any tools are executed.

TL;DR

A deep dive into Anthropic's Model Context Protocol, what it is, how it works, and why it matters for connecting AI models to external tools and data sources.

Key Takeaways

MCP is an open protocol by Anthropic that standardizes how AI models connect to external tools and data sources.

It uses a client-server architecture where AI apps (clients) connect to MCP servers that expose tools, resources, and prompts.

MCP eliminates the N*M integration problem, build one MCP server per tool, and any MCP-compatible client can use it.

The protocol supports both local (stdio) and remote (SSE/streamable HTTP) transport mechanisms.

Free Resources

Download free guides, cheatsheets, and templates curated from 130+ tutorials on RAG, AI Agents, and Prompt Engineering.

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Model Context Protocol (MCP) Explained

TL;DR

Key Takeaways

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