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# https://handbook.exemplar.dev/ llms.txt

- [AI Engineer's Handbook](https://handbook.exemplar.dev/): AI Engineering Handbook for developers, product leaders, and architects; covering core concepts, LLMs, and mainframe integration.
- [AI Engineer Role & Responsibilities](https://handbook.exemplar.dev/ai_engineer): Overview of AI engineering, key differences from ML engineering, core concepts, and resources.
- [LLM Pitfalls and Best Practices](https://handbook.exemplar.dev/ai_engineer/llms/pitfalls_llm): Avoid common LLM issues; learn prompt engineering, bias mitigation, and safety measures for reliable AI.
- [AI Image Prompting](https://handbook.exemplar.dev/ai_engineer/prompt_engineering/image_prompting): Master image generation with AI models like DALL-E and Stable Diffusion using effective prompt engineering techniques and iterative refinement.
- [LLMOps: Managing Large Language Models](https://handbook.exemplar.dev/ai_engineer/llms/llm_ops): Deploying, monitoring, and maintaining LLMs in production; encompassing model versioning, deployment, monitoring, and maintenance best practices.
- [AI Vector Databases Guide](https://handbook.exemplar.dev/ai_engineer/vector_dbs): Learn vector database architecture, embedding models, similarity search, indexing, and scaling strategies.  Explore popular solutions and advanced topics.
- [LLM Playgrounds and Tools](https://handbook.exemplar.dev/ai_engineer/dev_tools/playgrounds): Explore various LLM playgrounds, prompt hubs, and model comparison tools for AI development and prompt engineering.
- [llms.txt Proposal](https://handbook.exemplar.dev/ai_engineer/llms/llms_txt): Structured file enhancing LLM interaction with web content, offering concise website overviews for efficient information processing.
- [AI Agent Integrations](https://handbook.exemplar.dev/ai_engineer/integration_patterns): Explore AI integration patterns and workflows for seamless AI agent implementation.
- [AI Agents Handbook](https://handbook.exemplar.dev/ai_engineer/ai_agents): Comprehensive guide to AI agents: types, anatomy, building, best practices, use cases, and resources. Includes various frameworks and advanced topics like memory and planning.
- [AI Newsletter Subscription](https://handbook.exemplar.dev/subscribers): Subscribe for AI, LLM, and Vector DB insights.
- [LLM Reliability Guide](https://handbook.exemplar.dev/ai_engineer/llms/reliability): Guide to LLM reliability, robustness, and best practices, covering challenges, research, and resources for building dependable AI models.
- [AI Model Evaluation Tools](https://handbook.exemplar.dev/ai_engineer/dev_tools/evaluation_tools): Evaluate AI models using these tools: Deepeval, UpTrain, and Trulens.
- [Effective AI Agents](https://handbook.exemplar.dev/ai_engineer/ai_agents/effective_agents): Guide to building effective AI agents, covering workflows, agent types, use cases, common patterns, best practices, real-world applications, and framework considerations.
- [Semantic vs. Similarity Search](https://handbook.exemplar.dev/ai_engineer/vector_dbs/similarity_semantic): Compare semantic and similarity search techniques,  applications, and challenges in vector databases.
- [Basic Prompting Guide](https://handbook.exemplar.dev/ai_engineer/prompt_engineering/basic_prompting): Learn fundamental prompt engineering techniques for effective AI interaction and improved results.
- [Multi-Modal AI Guide](https://handbook.exemplar.dev/ai_engineer/llms/multi_modal_ai): Multi-modal AI models process various data types (text, images, audio, video) simultaneously, offering improved accuracy and broader applications.
- [Prompt Hub Guide](https://handbook.exemplar.dev/ai_engineer/prompt_engineering/prompt_hub): Centralized repository for storing, managing, and organizing prompts used with LLMs; enables collaboration, version control, and quality assurance.
- [LLM Settings Guide](https://handbook.exemplar.dev/ai_engineer/llms/llm_settings): Master LLM parameters like temperature, Top-P, and penalties for improved AI outputs.
- [Vector Database Similarity Search](https://handbook.exemplar.dev/ai_engineer/vector_dbs/similarity_search): Learn similarity search in vector databases, distance metrics, indexing, optimization, and best practices.
- [AI Dev Miscellaneous Tools](https://handbook.exemplar.dev/ai_engineer/dev_tools/miscellaneous_tools): Useful miscellaneous tools for AI engineers, including prompt engineering and data generation.
- [AI Agent Further Reading](https://handbook.exemplar.dev/ai_engineer/ai_agents/further_reading): Additional resources and links for learning about AI agents.
- [RAG System Anatomy](https://handbook.exemplar.dev/ai_engineer/rag/rag_anatomy): Anatomy of a RAG system: components, workflows, and considerations for effective implementation.
- [AI for Product Leaders](https://handbook.exemplar.dev/ai_product_leaders): Guide for product leaders on leveraging AI; resources coming soon.
- [GenAI Development Frameworks](https://handbook.exemplar.dev/ai_engineer/dev_tools/frameworks): Overview of popular frameworks for building large language model applications, including LangChain, LlamaIndex, Haystack, and more.
- [AI Agent Notes](https://handbook.exemplar.dev/ai_engineer/ai_agents/notes): AI agent design notes covering foundational models, memory, function calling, tool integration, best practices, common patterns, and helpful resources.
- [AI Engineer Further Reading](https://handbook.exemplar.dev/ai_engineer/further_reading): Curated books, courses, cookbooks, and learning paths for AI engineers.
- [Agentic RAG Explained](https://handbook.exemplar.dev/ai_engineer/rag/agentic_rag): Agentic RAG enhances traditional RAG with autonomous agents for complex, multi-step tasks using multiple tools and real-time data.
- [AI Security, Safety & Ethics](https://handbook.exemplar.dev/ai_engineer/ai_security_safety_ethics): AI security, safety, ethics guide covering fairness, accountability, privacy, technical and operational safety, ethical guidelines, security measures, and best practices for responsible AI development.
- [Open Source RAG Tools](https://handbook.exemplar.dev/ai_engineer/rag/open_source_rag_tools): Explore open-source libraries for building Retrieval Augmented Generation (RAG) systems.
- [AI Dev Tools & Resources](https://handbook.exemplar.dev/ai_engineer/dev_tools): Essential AI frameworks, LLMs, playgrounds, platforms, and evaluation tools for building robust AI applications.
- [Building AI Agents](https://handbook.exemplar.dev/ai_engineer/ai_agents/building_agents): Guide to building AI agents: core components (foundation models, memory, planning), implementation details, advanced capabilities, and best practices for integration.
- [Local LLMs: Tools and Setup](https://handbook.exemplar.dev/ai_engineer/dev_tools/local_llms): Guide to running large language models locally, including various tools, hardware requirements and considerations.
- [AI Agent Anatomy](https://handbook.exemplar.dev/ai_engineer/ai_agents/anatomy): Comprehensive guide to AI agent architecture, encompassing core components (sensors, processing unit, actuators), key characteristics, implementation considerations, best practices, and advanced capabilities.
- [AI Agent Types](https://handbook.exemplar.dev/ai_engineer/ai_agents/types): Comprehensive guide to various AI agent types, categorized by scope of work, capabilities, feasibility, and automation level.
- [Vector Databases Guide](https://handbook.exemplar.dev/ai_engineer/vector_dbs/database): Comprehensive guide to vector databases: architecture, functionalities, use cases, popular solutions, and best practices for implementation.
- [Agentic Document Workflows](https://handbook.exemplar.dev/ai_engineer/ai_agents/adw): AI agents autonomously process documents, make decisions, and execute actions, handling complex tasks end-to-end.  Workflows are automated, intelligent, and scalable.
- [AI Dev Platforms Guide](https://handbook.exemplar.dev/ai_engineer/dev_tools/dev_ai_platforms): AI development platforms: overview, comparison, and selection guide for AI engineers.
- [GenAI Integration Guide](https://handbook.exemplar.dev/ai_engineer/integration_patterns/genai_interaction): Guide to integrating GenAI: prompt pre-processing, inference, post-processing, results, and logging.
- [Prompt Engineering Necessity](https://handbook.exemplar.dev/ai_engineer/prompt_engineering/need_prompting): Understand the importance of prompt engineering for effective AI interaction and desired outputs.
- [Prompt Hacking: LLM Exploitation](https://handbook.exemplar.dev/ai_engineer/prompt_engineering/prompt_hacking): Learn prompt hacking techniques, types, defenses, and best practices for securing LLMs.
- [Prompt Engineering Guide](https://handbook.exemplar.dev/ai_engineer/prompt_engineering): Master prompt engineering techniques, tools, and best practices for effective LLM interaction.
- [Why Use Retrieval Augmented Generation?](https://handbook.exemplar.dev/ai_engineer/rag/why_rags): Learn about RAG's benefits: addressing LLM limitations, enabling real-time updates, and reducing hallucinations.
- [Cache Augmented Generation (CAG)](https://handbook.exemplar.dev/ai_engineer/cag): Faster, efficient alternative to RAG using caching for context-augmented text generation;  improves speed and resource usage.
- [AI Embeddings Guide](https://handbook.exemplar.dev/ai_engineer/embeddings): Master embedding models, similarity search, indexing, and scaling for AI applications.
- [Retrieval Augmented Generation (RAG) Architectures](https://handbook.exemplar.dev/ai_engineer/rag/paradigms_of_rags): Explore naive, advanced, and modular RAG paradigms, comparing complexity, accuracy, and flexibility.
- [AI Entrepreneurship Guide](https://handbook.exemplar.dev/ai_entrepreneurship): Master AI for business, from workflow consulting to building custom solutions and SaaS products.
- [RAG Design Patterns](https://handbook.exemplar.dev/ai_engineer/rag/types_of_rag): Explore various Retrieval-Augmented Generation (RAG) design patterns, including basic, advanced, specialized processing, and analysis types, along with selection guidelines and references.
- [Embeddings Introduction](https://handbook.exemplar.dev/ai_engineer/embeddings/introduction): Understanding embeddings, their applications, and best practices for semantic search and RAG.
- [RAG vs. Fine-tuning](https://handbook.exemplar.dev/ai_engineer/rag/rag_vs_fine_tuning): Compare RAG and fine-tuning for LLMs: advantages, use cases, and a decision framework to choose the best approach for your needs.
- [Building LLMs](https://handbook.exemplar.dev/ai_engineer/llms/llm_concepts/how_llms_built): Learn how Large Language Models are built: data collection, tokenization, model architecture, training, and fine-tuning.  Includes numerous additional resources.
- [Understanding Large Language Models](https://handbook.exemplar.dev/ai_engineer/llms/llm_concepts/what_is_llm): Introduction to LLMs: architecture, types, capabilities, applications, and resources.
- [ML Roadmap 2025](https://handbook.exemplar.dev/ai_ml_roadmap): Master machine learning by 2025 with this roadmap covering fundamentals, prerequisites, algorithms, and real-world projects.
- [LLM Guide for AI Engineers](https://handbook.exemplar.dev/ai_engineer/llms): Comprehensive guide to Large Language Models: concepts, development, operations, and advanced topics.
- [AI on IBM Mainframes](https://handbook.exemplar.dev/ai_mainframe): Learn about AI capabilities, applications, and benefits of integrating AI on IBM Z mainframes.
- [LLM Vocabulary](https://handbook.exemplar.dev/ai_engineer/llms/llm_concepts/vocab): Glossary of core LLM concepts, RAG components, agents, ethics, security, and learning paradigms.
- [AI Agent Use Cases](https://handbook.exemplar.dev/ai_engineer/ai_agents/use_cases): AI agent use cases across various departments, including sales, marketing, recruiting, engineering, and more, with specific tools listed.
- [AI Consulting & Strategy](https://handbook.exemplar.dev/consult): Expert AI consulting services for LLMs, RAGs, prompt engineering, and AI security.
- [LLM 2.0: Next Generation LLMs](https://handbook.exemplar.dev/ai_engineer/llms/llm_2_0): Enhanced reasoning, accuracy, and context handling in large language models.
- [AI Agent Tool Comparison](https://handbook.exemplar.dev/ai_engineer/ai_agents/agent_tools): Compare LangChain, Autogen, Crew AI, OpenAI Swarm, and Agentarium:  features, strengths, weaknesses, and best use cases for each AI agent framework.
- [Retrieval Augmented Generation (RAG)](https://handbook.exemplar.dev/ai_engineer/rag): Learn RAG implementation, evaluation, and best practices; explore core concepts, various types, and agentic RAG, comparing it to fine-tuning.  Includes helpful resources and a roadmap.
- [Advanced Prompting Techniques](https://handbook.exemplar.dev/ai_engineer/prompt_engineering/prompting_techniques): Master various prompting techniques like Chain-of-Thought, Few-Shot, and Role Prompting to enhance AI interactions.
- [Pre-trained Language Models](https://handbook.exemplar.dev/ai_engineer/llms/pre_trained_models): Overview of popular proprietary and open-source LLMs; categorized by size, access, capability, and selection criteria.