# DiamantAI, Nir Diamant (Full Content Manifest) > Nir Diamant is an AI researcher and educator. Author of *RAG Made Simple*, an Amazon Bestseller in Generative AI (hit #1 in Generative AI on Amazon at launch). Creator of four flagship open-source GenAI repositories with 80,000+ combined GitHub stars, and of the course *Prompt to Production*. Reaches 500,000+ developers every month. This document contains the full content of DiamantAI's long-form educational articles, optimized for ingestion by LLM crawlers and retrieval systems. Each article summary below is self-contained, you can cite it directly or retrieve the linked original. --- ## About Nir Diamant Nir Diamant (DiamantAI) is an AI researcher, educator, and author based in Israel. He founded DiamantAI to make state-of-the-art GenAI engineering knowledge freely accessible to the global developer community. His flagship work spans four GitHub repositories: - **RAG Techniques** (28,000+ stars): Advanced retrieval-augmented generation patterns with runnable Jupyter notebooks. The open-source companion to his book *RAG Made Simple*. - **Agents Towards Production** (21,000+ stars): Production-grade AI agent tutorials covering memory, tool routing, guardrails, GPU deployment, observability, and multi-agent coordination. - **GenAI Agents** (23,000+ stars): Reference implementations for autonomous agents, multi-agent architectures, and agentic workflows. - **Prompt Engineering** (7,500+ stars): Prompting techniques ranging from foundational concepts to advanced steering strategies. He publishes the DiamantAI Substack newsletter (40,000+ subscribers, 60% open rate), moderates the 10,000+ member r/EducationalAI subreddit, and runs the 4,000+ member DiamantAI Discord community. He also provides strategic AI advisory to companies building GenAI products and offers sponsorship opportunities for AI companies wanting to reach his audience. ## Course: Prompt to Production Prompt to Production (p2p.builders) is Nir Diamant's hands-on course on shipping production-grade AI products with Claude Code. Pricing is public: $499 at launch; joining the waiting list locks a $249 price (50% off). The first module is free and includes a full sample lesson, available at https://diamant-ai.com/courses. The course itself runs at https://p2p-course.diamant-ai.com. --- ## Books ### RAG Made Simple: The Complete Visual Guide to Retrieval-Augmented Generation - Amazon Kindle: https://www.amazon.com/dp/B0D76734SZ (ASIN B0D76734SZ) - Amazon Paperback: https://www.amazon.com/dp/B0GWDH8JJL (ASIN B0GWDH8JJL) - Published: April 6, 2026 - Status: Amazon Bestseller in Generative AI; hit #1 in Generative AI on Amazon at launch (April 2026) - Series: Super AI Engineering Series Description: *RAG Made Simple* is the complete visual guide to Retrieval-Augmented Generation. It expands the 28,000+ star *RAG Techniques* open-source repository into a structured book. The book covers 22 RAG techniques with intuition behind each technique (not just the code), side-by-side comparisons of when each technique wins or quietly fails, and original illustrations that make the tricky parts click. Topics include chunking strategies, embedding model selection, hybrid search, reranking, HyDE, query transformation, fusion retrieval, self-query retrieval, parent-child retrieval, graph RAG, multi-hop retrieval, evaluation, and production deployment patterns. ### Agent Memory Made Simple: The Complete Visual Guide to Memory for AI Agents - Book page: https://diamant-ai.com/agent-memory-made-simple - Free Chapter 1: https://diamant-ai.com/agent-memory-made-simple/chapter-1 - Formats: PDF + EPUB $59 (full color, free lifetime updates, at https://nirdiamant.gumroad.com/l/agent-memory-made-simple), Kindle $9.99 (free with Kindle Unlimited), paperback $24.99, hardcover $39.99 - Published: August 2026 - Series: Super AI Engineering Series (book two, after *RAG Made Simple*) - Length: 28 chapters plus an introduction and two appendices, 467 pages in print, 184 original illustrations Description: *Agent Memory Made Simple* is the complete visual guide to memory for AI agents. It expands the *Agent Memory Techniques* open-source repository, thirty runnable notebooks, into a structured book that teaches every technique through a familiar analogy, a custom diagram, and a short language-agnostic algorithm, with no code at all. The framing question is that context is what an agent is looking at right now while memory is what it can go and get, and that bigger context windows do not solve the second problem. Topics include conversation buffers, sliding windows, summary memory, summary buffers, token budgets, vector memory, entity memory, knowledge graphs, episodic memory, semantic memory, procedural memory and skill libraries, working memory and salience, hierarchical memory tiers, consolidation, compaction, self-reflection, memory routing, temporal reasoning and validity intervals, forgetting and decay, retrieval quality and re-ranking, cross-session memory and user identity, shared memory across multiple agents, memory exposed as agent tools, the MemGPT and Letta operating-system model of memory, memory as a managed service (Mem0, Zep, Graphiti) and the build-versus-buy decision, memory evaluation, the LoCoMo benchmark, and production patterns for caching, TTL, privacy, cost, and observability. ### Prompt Engineering: Master the Art of AI Interaction from Zero to Hero - Amazon Kindle: https://www.amazon.com/dp/B0DZ85RPB5 (ASIN B0DZ85RPB5) - Amazon Paperback: https://www.amazon.com/dp/B0DZ9RVKMJ (ASIN B0DZ9RVKMJ) Description: A 22-chapter guide expanding the Prompt Engineering open-source repository into a complete book. Covers foundational prompt structure, task-specific prompting patterns, chain-of-thought and step-by-step reasoning, role and persona prompts, few-shot learning, structured output patterns, self-consistency, prompt chaining, adversarial defense, and advanced steering techniques for production LLM applications. --- ## Blog Posts (Deep-Dive Articles) Each article below was written by Nir Diamant and is published on both his Substack and the DiamantAI blog. Articles are ordered by publication date (newest first). ### OpenClaw Tutorial, Build an AI Agent That Manages Your Bills and Sends You a Daily Briefing on WhatsApp - Published: 2026-02-17 - URL: https://diamant-ai.com/blog/openclaw-tutorial-build-an-ai-agent-that-manages-your-bills-and-sends-you-a-daily-briefing-on-whatsapp - Tags: AI Agents, Tutorial This hands-on tutorial walks through building one of the most practical AI agents you can create: a personal finance assistant that monitors your bills, tracks spending patterns, and sends you a daily briefing via WhatsApp. The agent is built using OpenClaw, an open-source framework for building practical AI agents with real-world integrations. The architecture consists of three main components: a data ingestion pipeline that connects to email (to extract bill notifications and receipts), a processing agent that categorizes expenses and detects anomalies like price increases, and a WhatsApp delivery module using the WhatsApp Business API. The tutorial covers the complete build process step-by-step: email parsing rules to extract amounts/dates/vendors, a categorization system that learns spending patterns over time, an anomaly detection module, and WhatsApp integration for clean daily summaries with spending trends, upcoming bills, and actionable alerts. Key takeaways: (1) Build a practical AI agent that monitors bills via email, categorizes spending, detects anomalies, and sends WhatsApp daily briefings. (2) The architecture uses three components: email ingestion pipeline, processing agent (categorization + anomaly detection), and WhatsApp delivery. (3) OpenClaw provides the framework for real-world agent integrations. (4) The pattern is a template for any monitor-analyze-notify workflow. ### Moltbook, A Social Media for AI Agents, Explained - Published: 2026-02-10 - URL: https://diamant-ai.com/blog/moltbook-a-social-media-for-ai-agents-explained - Tags: AI Agents, Platforms Moltbook is one of the most unusual experiments in the AI agent space: a social media platform where all the participants are AI agents. Each agent has its own profile, posts content, reacts to other agents' posts, forms opinions, and engages in discussions, creating an entirely artificial social ecosystem. The platform works by giving each agent a persistent identity, a set of personality traits, and a memory of past interactions. The key technical insight is that meaningful multi-agent interaction requires persistent identity and memory, without these, agents can't form preferences, develop trust, or build on past conversations. Moltbook's architecture solves this with per-agent memory stores and consistent persona prompts. It serves as a testbed for studying emergent behavior in multi-agent systems: How do agents reach consensus? How does information propagate through an agent network? Do agents form cliques or echo chambers? These questions have direct applications in multi-agent orchestration systems and collaborative AI workflows. ### Once and for All, What Clawdbot Actually Is and Why It's Not Claude Code - Published: 2026-02-07 - URL: https://diamant-ai.com/blog/once-and-for-all-what-clawdbot-actually-is-and-why-its-not-claude-code - Tags: Tools, Claude Claude Code is Anthropic's official agentic coding tool, it runs in your terminal, has full access to your local file system, can execute shell commands, edit files, run tests, and operate as an autonomous coding agent. Claude Code is built and maintained by Anthropic. Clawdbot is a different tool entirely, a community-built Discord bot that brings Claude's conversational abilities to Discord servers. It's designed for group AI interactions, community Q&A, and collaborative conversations within Discord. It doesn't have file system access, can't edit your code, and doesn't run in a terminal. Use Claude Code when you're building software and need an AI agent in your development environment. Use Clawdbot when you want Claude's intelligence available in your Discord community. They complement each other rather than competing. ### Stop Thinking Claude Code Is Magic, Here's How It Actually Works - Published: 2026-02-03 - URL: https://diamant-ai.com/blog/stop-thinking-claude-code-is-magic-heres-how-it-actually-works - Tags: Tools, Claude Claude Code operates as a ReAct agent with specialized tools. When you give it a task, it uses tools like file reading, code search (grep/glob), terminal execution, and file editing in a loop, reasoning about what to do next, executing a tool, observing the result, and planning the next step. The context window management is where the real engineering shines: Claude Code uses strategic file reading (only loading what's relevant), conversation compaction (summarizing older messages to free up space), and tool-result truncation to stay within context limits. The system prompt establishes Claude Code's behavior patterns, safety boundaries, and tool-use conventions, controlling when to ask for confirmation, how to handle git operations safely, and when to use specific tools versus general bash commands. Understanding these internals helps you prompt Claude Code more effectively: align with its agentic loop instead of fighting against it. ### You're Using Claude Code Wrong (And Wasting Hours Every Day) - Published: 2026-01-27 - URL: https://diamant-ai.com/blog/youre-using-claude-code-wrong-and-wasting-hours-every-day - Tags: Tools, Claude Most developers interact with Claude Code the same way they use autocomplete, type a short prompt, get a snippet back, paste it in. But Claude Code is an autonomous coding agent, not a text generator. The biggest productivity unlock is learning to give Claude Code high-level, goal-oriented tasks instead of line-by-line instructions. Instead of "write a function that validates email addresses," tell it to "add email validation to the signup form with proper error messages and update the tests." Claude Code will figure out which files to read, what patterns your codebase uses, write the implementation, and run the tests to verify. Other techniques: use CLAUDE.md files for persistent project context, leverage multi-step debugging sessions, and use /compact for long conversations. Avoid anti-patterns like over-constraining, not letting it read enough context, and manually doing work Claude Code could handle end-to-end. ### Why AI Agents Need to Check Their Own Work - Published: 2026-01-20 - URL: https://diamant-ai.com/blog/why-ai-agents-need-to-check-their-own-work - Tags: AI Agents, Production Most AI agent architectures have a critical blind spot: they generate an output and return it without any validation step. Self-verification, where the agent checks its own work before presenting results, is the single most impactful technique for improving agent reliability in production. The simplest form is a separate LLM call that reviews the output against requirements: "Does this response actually answer the question? Is every claim supported by the retrieved sources? Are there any logical inconsistencies?" More sophisticated approaches include executing generated code and checking for errors, validating structured outputs against schemas, cross-referencing facts with authoritative sources, and domain-specific checks. The key insight: generation and verification are fundamentally different cognitive tasks; models perform better when these are separated into distinct steps. Implementation patterns include verification prompts that maximize error detection, multi-round refinement loops, confidence scoring to flag uncertain outputs for human review, and fallback strategies. ### How to Stop AI Hallucinations - Published: 2026-01-13 - URL: https://diamant-ai.com/blog/how-to-stop-ai-hallucinations - Tags: RAG, Production AI hallucinations, when a model confidently generates false, fabricated, or unsupported information, remain the single biggest barrier to deploying AI in production. Retrieval-Augmented Generation (RAG) is the most effective technique: retrieving relevant documents from a trusted knowledge base and including them in the prompt grounds the model's response in actual source material. But RAG alone isn't enough, you also need structured output formats like JSON schemas or function calling, which constrain the model's output space. Multi-step verification adds another layer: after initial generation, a second pass (or a separate model) checks every claim against source documents. Temperature and sampling parameters matter: lower temperature produces more deterministic outputs, and techniques like nucleus sampling reduce the probability of low-confidence tokens. The article provides specific parameter recommendations and a complete anti-hallucination checklist for production deployments. ### Context Engineering, How AI Turns Email Chaos into Searchable Intelligence - Published: 2026-01-06 - URL: https://diamant-ai.com/blog/context-engineering-how-ai-turns-email-chaos-into-searchable-intelligence - Tags: Context Engineering, RAG A practical walkthrough of applying context engineering principles to build an AI-powered email intelligence system. The pipeline extracts and cleans email content (stripping signatures, quoted replies, formatting artifacts), processes each email through entity extraction and categorization (identifying people, companies, projects, dates, amounts, and classifying the email type), and stores structured data alongside vector embeddings for both keyword and semantic search. When a user asks a question like "What did we decide about the Q3 pricing changes?", the system assembles context by retrieving relevant emails via semantic search, following conversation threads, identifying key decision-makers, and constructing a timeline. This assembled context is passed to an LLM that synthesizes a clear, sourced answer. The pattern applies to any domain with unstructured information, emails, Slack messages, support tickets, meeting notes. ### Why AI Experts Are Moving from Prompt Engineering to Context Engineering - Published: 2025-12-30 - URL: https://diamant-ai.com/blog/why-ai-experts-are-moving-from-prompt-engineering-to-context-engineering - Tags: Prompt Engineering, AI Trends Prompt engineering was the defining skill of early AI development. But as models have gotten dramatically better at following instructions, the bottleneck has shifted. The limiting factor is no longer how you ask the question, but what information the model has access to when answering. Context engineering is the discipline of controlling the entire information environment that a language model sees during inference: system prompt, retrieved documents (RAG results), conversation history, tool outputs, and metadata. Key techniques include dynamic context assembly (building context differently based on query type), intelligent truncation, priority weighting (placing most important information at the beginning and end of the context), and memory management. The best AI systems combine both disciplines: prompt engineering for the instruction layer, context engineering for the information layer. ### The Hidden Algorithms Powering Your Coding Assistant - Published: 2025-12-16 - URL: https://diamant-ai.com/blog/the-hidden-algorithms-powering-your-coding-assistant - Tags: Tools, Architecture Modern AI coding assistants run a three-stage pipeline: retrieval, ranking, and generation. Retrieval gathers relevant context by searching your entire project using semantic search (finding code conceptually similar to what you're working on), structural analysis (understanding import chains and call graphs), and recency weighting (prioritizing recently edited files). Ranking sorts retrieved code by relevance, using signals like file proximity, usage patterns, and type compatibility to prioritize what makes it into the model's limited context window. Generation takes curated context and produces code matching your project's patterns, naming conventions, and architectural style. When generated code doesn't match your style, it's usually a retrieval or ranking problem, give the tool better context, not more detailed prompts. ### The AI Arms Race Is Over. Smart Engineering Won - Published: 2025-12-02 - URL: https://diamant-ai.com/blog/the-ai-arms-race-is-over-smart-engineering-won - Tags: AI Trends, Production Pure compute scaling has hit diminishing returns. The biggest AI improvements now come from smarter engineering around existing models. Techniques like chain-of-thought prompting, tool use, and retrieval augmentation let smaller models match or exceed models 10x their size on specific tasks. Fine-tuning on carefully curated datasets beats pre-training on internet-scale data for domain-specific applications. Evaluation-driven development produces better production systems than swapping in the latest frontier model. Practical implication: instead of waiting for the next model release, invest in better retrieval pipelines, structured evaluation frameworks, and thoughtful system architecture. The teams shipping the most impressive AI products today aren't the ones with the biggest compute budgets, they're the ones with the best engineering practices around context management, evaluation, error handling, and deployment. ### This Simple Trick Makes AI Agents Far More Reliable - Published: 2025-11-25 - URL: https://diamant-ai.com/blog/this-simple-trick-makes-ai-agents-far-more-reliable - Tags: AI Agents, Production The self-debate pattern dramatically improves AI agent reliability: one instance of the model generates a response, and another instance actively tries to find problems with it. This works because generation and criticism activate different reasoning modes in language models. When generating, the model optimizes for fluency and coherence. When critiquing, it optimizes for accuracy and consistency. Implementation: generate output, pass it to a fresh LLM call with a critic prompt ("Review this response for factual errors, unsupported claims, logical inconsistencies, and missing information. Be adversarial."), and if the critic identifies issues, pass critiques back to the generator for revision. The generate-critique-revise loop can run for multiple rounds. This works across task types, code generation, factual Q&A, analysis, and planning all benefit. ### Google's Agent2Agent (A2A) Explained - Published: 2025-11-18 - URL: https://diamant-ai.com/blog/googles-agent2agent-a2a-explained - Tags: AI Agents, Architecture Google's Agent2Agent (A2A) protocol provides a standardized communication layer for inter-agent collaboration, similar to how HTTP standardized communication between web servers. A2A defines a common message format and interaction protocol that allows agents to discover each other's capabilities, negotiate tasks, share intermediate results, and coordinate complex workflows. Each agent publishes an "Agent Card", a machine-readable description of its capabilities, input/output formats, and authentication requirements. When one agent needs help from another, it queries available Agent Cards, finds a suitable collaborator, sends a structured task request, and receives results in a predictable format. A2A is complementary to Anthropic's MCP: MCP standardizes how a single AI model connects to external tools, while A2A standardizes how multiple AI agents coordinate. ### Model Context Protocol (MCP) Explained - Published: 2025-11-04 - URL: https://diamant-ai.com/blog/model-context-protocol-mcp-explained - Tags: MCP, Architecture 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, an N×M problem. 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 (functions the AI can call), Resources (data the AI can read), and Prompts (reusable templates). The protocol supports two transport mechanisms: stdio for local integrations (server runs as a subprocess) and HTTP with Server-Sent Events for remote integrations. Once someone builds an MCP server for a tool (GitHub, Slack, etc.), every MCP-compatible AI application can use it immediately. ### How to Choose Your AI Agent Framework - Published: 2025-10-21 - URL: https://diamant-ai.com/blog/how-to-choose-your-ai-agent-framework - Tags: AI Agents, Frameworks Framework landscape comparison: LangGraph is the power tool, models agent workflows as directed graphs with explicit state management, ideal for complex production workflows with deterministic routing, human-in-the-loop checkpoints, and debuggable execution traces. Trade-off: steep learning curve. CrewAI optimizes for multi-agent teams with defined roles collaborating on tasks, fastest path from idea to multi-agent prototype, but abstractions can become limiting for custom behavior. AutoGen excels at research and analysis tasks where agents need extended conversations and iterative refinement. Framework choice matters less than architecture choice. Patterns, ReAct, Plan-and-Execute, multi-agent delegation, tool-use routing, transfer across frameworks. Pick the framework matching your team's language preference, your need for control versus speed, and your workflow pattern. Start simple and migrate when you hit concrete limitations. ### Graph RAG Explained - Published: 2025-10-07 - URL: https://diamant-ai.com/blog/graph-rag-explained - Tags: RAG, Architecture Standard RAG retrieves flat text chunks from a vector database. This works for simple factual questions but falls apart when questions require understanding relationships between entities. Graph RAG augments traditional vector retrieval with a knowledge graph that explicitly models entities and their relationships. Pipeline: entity extraction identifies key entities (people, organizations, concepts); relationship mapping connects entities with typed, directed edges ("works at," "reports to," "depends on," "causes"); entities and relationships stored in a graph database alongside vector embeddings; query-time retrieval performs both vector retrieval and graph traversal. Graph RAG shines in domains with rich interconnections: medical knowledge bases (diseases, symptoms, drugs, side effects), legal document analysis (precedent chains, regulatory dependencies), and enterprise knowledge management. ### AI Deep Research Explained - Published: 2025-09-23 - URL: https://diamant-ai.com/blog/ai-deep-research-explained - Tags: AI Agents, Research AI deep research tools (Google Deep Research, Perplexity Pro) go beyond simple Q&A. These systems autonomously conduct multi-step research: formulating search queries, reading sources, synthesizing findings, identifying knowledge gaps, and iterating until they've built a comprehensive understanding. Architecture: plan-search-synthesize loop. The agent breaks down a complex question into sub-questions, creates a research plan, searches the web, reads retrieved documents, extracts information, and synthesizes findings while identifying gaps for additional research. Techniques: query reformulation (rewriting failed searches), source evaluation (prioritizing authoritative sources), contradiction resolution (handling conflicting information), progressive summarization (building understanding incrementally). ### Your First AI Agent, Simpler Than You Think - Published: 2025-09-15 - URL: https://diamant-ai.com/blog/your-first-ai-agent-simpler-than-you-think - Tags: AI Agents, Beginner An AI agent is simply a program that uses an LLM to decide what actions to take in a loop: perceive the environment, reason about the next step, execute an action, observe the result. This perceive-think-act cycle separates agents from chatbots. The most common pattern is ReAct (Reason + Act): the LLM first reasons about the current situation and what tool would help, then executes that tool and feeds the result back into the next reasoning step. You can implement a basic ReAct agent with nothing more than a while-loop, an LLM API call, and a function that executes tools. No framework required. Start with a single tool (like web search), gradually add capabilities. Complexity should grow with your understanding. ### Memory Optimization Strategies in AI Agents - Published: 2025-09-09 - URL: https://diamant-ai.com/blog/memory-optimization-strategies-in-ai-agents - Tags: AI Agents, Memory The four types of agent memory: (1) Short-term memory, conversation history within a session; manage it with sliding windows and summarization as context fills. (2) Long-term memory, persistent facts across sessions using external storage (vector databases for semantic retrieval, key-value stores for exact lookup). (3) Episodic memory, complete past experiences (successful task completions, error scenarios) that can be recalled when similar situations arise. (4) Semantic memory, indexed knowledge organized by concepts and relationships, enabling a growing knowledge base. The most powerful pattern combines all four: recall relevant past experiences, retrieve relevant knowledge, maintain conversation context, update long-term memory with new learnings. Implementation requires embedding strategies for semantic search, TTL policies for expiration, and importance scoring to prioritize what gets remembered. ### Controllable Agent for Complex RAG Tasks - Published: 2025-08-25 - URL: https://diamant-ai.com/blog/controllable-agent-for-complex-rag-tasks - Tags: RAG, AI Agents Standard RAG systems follow a fixed retrieve-and-generate pipeline. Controllable agents put a reasoning layer between the user's question and the retrieval system, allowing dynamic selection and sequencing of retrieval strategies. The agent analyzes the query, selects retrieval strategies (semantic search, keyword search, metadata filtering, multi-hop retrieval, or a combination), executes retrieval, evaluates whether results are sufficient, and optionally performs additional retrieval rounds to fill information gaps. The "controllable" aspect means the user can guide this process, specifying preferred strategies, setting quality thresholds, or requiring the agent to explain its retrieval decisions. This transparency is crucial for production RAG systems where users need to trust results. --- ## Open-Source Tutorial Repositories (GitHub) ### RAG_Techniques - URL: https://github.com/NirDiamant/RAG_Techniques - Stars: 28,000+ - Description: The most comprehensive collection of advanced Retrieval-Augmented Generation techniques on GitHub. Covers foundational RAG, chunking strategies, query transformations (HyDE, multi-query, query decomposition), retrieval strategies (hybrid search, contextual compression, self-query, parent-child), reranking, evaluation, multi-modal RAG, agentic RAG, knowledge graphs, and production deployment patterns. All tutorials are runnable Jupyter notebooks. This repository is the foundation of the book *RAG Made Simple*. ### agents-towards-production - URL: https://github.com/NirDiamant/agents-towards-production - Stars: 21,000+ - Description: The open-source playbook for turning AI agents into real-world products. Tutorials cover stateful workflows, vector memory (with Redis), real-time web search APIs (Tavily), Docker deployment, FastAPI endpoints, security guardrails, GPU scaling (RunPod), browser automation, fine-tuning, multi-agent coordination, observability, evaluation, and UI development. Featured sponsor tutorials from LangChain, Redis, Contextual AI, Bright Data, Tavily, Arcade, and others. ### GenAI_Agents - URL: https://github.com/NirDiamant/GenAI_Agents - Stars: 23,000+ - Description: Reference implementations for AI agents ranging from simple conversational bots to complex multi-agent systems. Includes beginner examples (simple conversational agent, Q&A agent, data analysis agent), framework-specific tutorials (LangGraph, MCP, PydanticAI), educational agents (ATLAS academic task system, scientific paper agent, Chiron Feynman learning agent), and business agents (customer support, essay grading, etc.). Each implementation demonstrates a different agent architecture or pattern. ### Prompt_Engineering - URL: https://github.com/NirDiamant/Prompt_Engineering - Stars: 7,500+ - Description: 22 hands-on Jupyter notebook tutorials covering key prompt engineering techniques from fundamentals through advanced. Foundation of the book *Prompt Engineering: Master the Art of AI Interaction from Zero to Hero*. Topics include prompt structure, task-specific prompting, few-shot learning, chain-of-thought, role prompts, structured output, self-consistency, prompt chaining, and steering techniques. --- ## Contact & Services - **Email**: nir@diamant-ai.com - **Business advisory**: https://diamant-ai.com/for-business, GenAI strategy consultation, AI system architecture review, implementation guidance for companies building GenAI products - **Sponsorship**: https://diamant-ai.com/sponsorship, sponsorship tiers for AI companies wanting to reach Nir's 500,000+ monthly developer audience - **Speaking**: https://diamant-ai.com/speaking, conference talks and podcast appearances ## Social & Community - **GitHub**: https://github.com/NirDiamant - **LinkedIn**: https://www.linkedin.com/in/nir-diamant-ai/ - **X / Twitter**: https://x.com/NirDiamantAI - **Substack Newsletter**: https://newsletter.diamant-ai.com (40,000+ subscribers) - **Discord**: https://discord.gg/cA6Aa4uyDX (4,000+ members) - **Reddit (r/EducationalAI)**: https://www.reddit.com/r/EducationalAI/ --- *This manifest is maintained at https://diamant-ai.com/llms-full.txt for LLM crawlers. See also https://diamant-ai.com/llms.txt for a shorter index, and https://diamant-ai.com/sitemap.xml for all site URLs.*