// TAG: LLM

Explore Loop Engineering as the evolution of prompt engineering, enabling AI agents to execute goal-driven workflows with tool access, feedback, and …

Understand the landscape of on-device AI agents and tiny LLM systems, set up your development environment, and explore core tooling for edge AI.

Unlock the power of Large Language Models by mastering foundational prompt engineering techniques. Learn to craft effective prompts, understand LLM …

A structured overview of the most important and trending AI engineering topics in 2026, covering agent systems, context engineering, infrastructure, …

Dive into Context Engineering for AI systems, understanding how to design, structure, and optimize context to enhance LLM performance, reliability, …

Explore the fundamentals of Retrieval-Augmented Generation (RAG), its typical architecture, and critical limitations that necessitate the evolution to …

Learn how to use Tunix, a JAX-native library for LLM post-training and specialization.

Learn to install and use LangExtract for structured data extraction from unstructured text.

Learn how to use the any-llm library to interact with various large language models easily and efficiently.

Dive into Quantization-Aware Training (QAT) for Gemma 4 models. Learn its principles, how it optimizes AI for mobile and laptop devices, and practical …

Master the art of crafting precise and secure prompts using system messages, effective delimiters, and structured output control for reliable LLM …

Explore the foundational concepts of LLM inference, including unique challenges, pipeline components, GPU optimization techniques, and crucial caching …

Dive deep into the LLM's context window, understanding its mechanics, limitations, and the critical role of tokenization in managing the LLM's …

Explore the foundational techniques of RAG 2.0, focusing on advanced embedding models and robust hybrid search strategies, including Reciprocal Rank …

Discover how Large Language Models (LLMs) serve as the 'brain' for autonomous AI agents, enabling reasoning, planning, and decision-making through API …

Learn how to connect LangExtract to LLM providers, securely manage API keys, and configure your development environment for advanced data extraction.

Explore Google's Gemma 4 family, including QAT variants, for optimizing AI model deployment on mobile and laptop devices. Learn about multimodal …

Learn how to integrate a tiny, quantized Large Language Model (LLM) directly onto an edge device for natural language understanding, enabling …

Unlock robust LLM reasoning with Chain-of-Thought and Self-Consistency. Learn to guide LLMs through complex problems, improving accuracy and …

Dive deep into advanced context assembly techniques for RAG 2.0. Learn to overcome simple chunking limitations, prevent context distortion, and build …

Discover how to equip your autonomous AI agents with external tools and APIs, enabling them to interact with the real world, perform actions, and …

Dive into effective context design for LLMs, learning how to structure information, manage data flow, and optimize inputs for superior AI performance …

Learn the principles and practices of crafting effective prompts to guide Large Language Models (LLMs) for specific tasks.

Learn how to define extraction tasks and schemas for data extraction using LangExtract and Pydantic.

Learn how to interact with LangCache using Node.js and Python, including initialization, storing prompts and responses.

Explore how autonomous AI agents manage memory, state, and persistent data storage to enable long-running, goal-driven workflows, overcoming LLM …

Explore how Flue Framework's stateful sessions enable context-aware AI agents for multi-turn interactions and complex tasks, with practical examples …

Dive deep into the planning and task decomposition mechanisms that enable autonomous AI agents to break down complex goals into manageable steps. …

Explore intelligent querying techniques in RAG 2.0, focusing on how Large Language Models (LLMs) enhance retrieval through query rewriting and enable …

Learn how to optimize LLM context by mastering reduction and summarization techniques, enhancing performance and reliability in production AI systems.

Learn how to systematically test and validate prompts for Large Language Models (LLMs) to ensure optimal performance, safety, and reliability in your …

Learn how to perform Supervised Fine-Tuning (SFT) with Tunix, a powerful tool for aligning LLMs.

Learn how to extend LLM capabilities with tools and function calling for real-world applications.

Learn how to use LangExtract for basic data extraction and understand the results.

Prepare your development environment, install necessary tools, and run your first inference with Google's Gemma 4 QAT models for optimized edge …

Integrate your on-device AI agent with smart home systems to execute real-world actions using local APIs and tiny LLMs for intent mapping.

Learn to build a Retrieval-Augmented Generation (RAG) system from scratch, covering document chunking, generating embeddings, and utilizing vector …

Dive deep into the reasoning core of autonomous AI agents. Learn how agents plan, solve problems, make decisions, and leverage advanced architectures …

Explore GraphRAG, an advanced RAG 2.0 technique. Learn how knowledge graphs enhance retrieval by modeling relationships, enabling multi-hop reasoning …

Learn how to design advanced schemas for data extraction using LangExtract, including nested structures and rich data types.

Learn about robust error handling and exception management in Python using the any-llm library for LLM interactions.

Build a cached LLM chatbot using Redis LangCache to minimize expensive API calls.

Master techniques for optimizing AI agent and tiny LLM performance and resource usage on constrained edge devices for real-world production …

Unpack the core components of an Agentic AI system: the LLM brain, crucial memory, external tools, and intelligent planning mechanisms. Learn how …

Dive deep into GraphRAG, learning how to build knowledge graphs, perform N-hop expansion, and integrate graph-based retrieval into your RAG 2.0 …

Explore short-term memory in Agentic AI systems, focusing on LLM context windows, conversation history management, and practical Python …

Explore AI Tool Marketplaces, how they empower AI agents with external capabilities, and their role in modern AI orchestration and development.

Dive into AI cost management, learning to track token usage and API expenses for Large Language Models (LLMs) and other AI services. Implement …

Learn how Tunix, built on JAX and Flax NNX, handles model architectures and state management for effective post-training.

Learn how to optimize a RAG application using LangCache for faster response times and reduced costs.

Learn how to build robust, secure, and error-tolerant on-device AI agents and tiny LLM systems using modern edge AI tooling as of early 2026.

Explore Retrieval-Augmented Generation (RAG) to overcome LLM limitations, integrate external knowledge, and build dynamic, multi-source context …

Learn how to detect and mitigate AI hallucinations in generative models like LLMs, ensuring reliability and trustworthiness in production systems.

Explore common insecure AI system design patterns and learn how to secure the AI supply chain from data to deployment, enhancing the resilience of …

Learn how to use the LangExtract API for structured information extraction with Python.

Learn how to guide LLMs towards structured outputs using any-llm, JSON mode, and function calling.

Unlock the full potential of omp.sh by learning advanced best practices, understanding its limitations, and comparing it to other AI coding agents to …

Dive deep into advanced agent architectures like ReAct, Reflection, and iterative planning-execution loops to build more robust and autonomous AI …

Explore best practices for deploying RAG 2.0 systems, learn crucial evaluation methodologies, and discover real-world applications to build robust and …

Learn how to proactively identify, analyze, and mitigate security threats in AI systems, especially Large Language Models and agentic applications, …

Learn how to implement basic RLHF workflows with Tunix for creating helpful and aligned Language Models.

Learn how to use interactive visualization and debugging techniques with LangExtract for accurate data extraction.

Learn how to integrate local AI models with Ollama and Docker for A2UI agents.

Master context control in AIPack to manage AI agent memory effectively, especially when working with large codebases. Learn RAG, chunking, and dynamic …

Explore persistent agent memory, distinguishing between short-term context and long-term knowledge bases for robust, production-ready AI agents. Learn …

Explore the world of multi-agent systems, learning how to design, coordinate, and orchestrate multiple autonomous AI agents to solve complex problems, …

Learn how to implement robust input and output guardrails, including safety filters, content moderation, and compliance checks, to ensure the …

Explore how AI agent frameworks manage short-term and long-term memory, and track workflow state to build intelligent, conversational, and persistent …

Learn how to scale large language models using Tunix and JAX for distributed training.

Learn how to use LangExtract's chunking strategies for efficient data extraction from long documents.

Explore loop engineering as the evolution of prompt engineering for autonomous AI agents, covering goal-driven loops, tool access, testing, feedback, …

Explore advanced memory management for AI agents, focusing on long-term context and knowledge retrieval using vector databases and Retrieval Augmented …

Explore how to design and build production-ready AI applications with a robust defense-in-depth security strategy, covering threat modeling, layered …

Build a practical AI observability system from scratch! Learn to instrument an LLM application with OpenTelemetry for tracing, metrics, and logs, then …

Learn how to optimize and profile your Tunix-powered LLM post-training for better performance.

Build a complete, production-grade harness for an AI coding agent, integrating environment setup, state management, control loops, tools, evaluation, …

Learn to rigorously evaluate and test your prompts and AI agents for accuracy, reliability, cost-efficiency, and safety in production environments.

Learn how to design and implement robust AI guardrail systems to ensure safety, reliability, and compliance for your AI applications in production.

Compare leading AI agent frameworks like LangGraph, AutoGen, CrewAI, and Semantic Kernel. Understand their core architectures, strengths, and …

Learn how to design, deploy, and manage production-ready autonomous AI agents, covering best practices for robustness, security, scalability, and …

Learn how to customize Tunix with custom loss functions, optimizers, and callbacks for advanced LLM post-training.

Learn how to handle errors, ensure robustness, and implement retries in your LangExtract pipelines for reliable data extraction.

Learn how to run Large Language Models locally using Ollama and integrate it with any-llm for seamless experimentation.

Learn how to build a robust, scalable, and cost-efficient Retrieval Augmented Generation (RAG) system using LLMOps best practices for production …

Explore the critical ethical considerations and robust control mechanisms essential for designing, deploying, and managing autonomous AI agents safely …

Explore the cutting-edge future trends in AI engineering, including hyper-specialized agents, self-improving systems, and decentralized AI, alongside …

Learn advanced RLHF strategies, focusing on Proximal Policy Optimization (PPO) with Tunix.

Learn about the unique security threats, privacy concerns, and ethical considerations in developing agentic AI systems using LLMs.

Build a dynamic multi-LLM chatbot using any-llm in Python.

Learn how to extend LangExtract with custom LLM providers for specialized, fine-tuned, or open-source models.

Learn to build an LLM-powered content summarizer using Python and the any-llm library.

Learn to align an LLM for factual accuracy using Tunix, a JAX-native framework.

Learn how to use LangExtract and Pydantic to extract key information from legal contracts.

Learn how to securely manage API keys for your LLM-powered applications using any-llm.

Learn how to effectively debug and troubleshoot Tunix workflows using JAX.

Learn how to deploy fine-tuned LLMs using FastAPI and Docker for efficient, scalable inference.

Learn how to extract structured data from e-commerce product listings using LangExtract and Pydantic.

Explore ethical considerations and responsible AI practices in the post-training phase of Large Language Models.

Learn advanced techniques for prompt engineering with LangExtract to achieve accurate data extraction.

Learn to avoid common pitfalls in data extraction using LangExtract and Large Language Models.

Learn how to deploy LangExtract in a production environment for reliable, efficient, and scalable data extraction.

Learn how to fine-tune a Large Language Model for a specific task using Parameter-Efficient Fine-Tuning techniques like LoRA.

Learn to optimize AI model deployment for mobile and laptop environments using Google's Gemma 4 Quantization-Aware Training (QAT) checkpoints, from …

Learn to build, deploy, and manage robust AI agents using the Flue Framework, focusing on its unique agent harness architecture, state management, and …

Learn to integrate omp.sh, an AI coding agent, into your terminal workflow. This guide covers installation, core commands, advanced features like plan …

This explainer clarifies recent LLM benchmark results, addressing claims of 0% scores and detailing actual performance on complex software engineering …

Comprehensive comparison of leading LLM API pricing models, including cost structures, token pricing, usage tiers, hidden fees, and optimization …

Deep technical explanation of how Multi-Token Prediction (MTP) works under the hood - architecture, internals, compilation, and real-world examples.

Explore and build three distinct on-device AI agents—a voice assistant, a data summarizer, and an anomaly detector—using tiny LLMs and modern edge …

The subtle yet significant changes in Claude Opus 4.7's system prompt fundamentally alter model behavior, demanding developers proactively adapt their …

Comprehensive comparison of OpenGPT (open-source) and OpenAI's custom ChatGPTs - features, performance, pros & cons, and when to use each in 2026.

Complete tutorial: Integrate VS Code with Ollama to create a local, private, and free AI coding assistant. Step-by-step guide with code examples for …

Google's TurboQuant algorithm slashes LLM KV cache memory by 6x and delivers up to 8x attention speedup with zero accuracy loss, significantly …

Explore the critical differences in scalability between Static Site Generators (SSGs) and Large Language Models (LLMs) in 2026, and learn when to …

Deep technical explanation of how TurboQuant works under the hood - architecture, internals, compilation, and real-world examples.

Explore the principles and practical applications of Agentic AI Systems, covering autonomous agents, planning, reasoning, tool usage, memory, and …

Learn to implement robust AI observability for production systems, covering logging, tracing, metrics, cost monitoring, and debugging of AI models and …

Learn to design, structure, and optimize context for Large Language Models (LLMs) to improve performance, reliability, and output quality in …

Learn to deploy and manage Large Language Models (LLMs) in production. This guide covers inference pipelines, model routing, caching, GPU …

Dive deep into modern RAG 2.0, exploring advanced techniques like hybrid search, GraphRAG, and multi-hop retrieval. Learn to overcome basic RAG …

Explore modern Retrieval-Augmented Generation (RAG 2.0) systems, mastering hybrid search, GraphRAG, multi-hop retrieval, and agentic strategies to …

Comprehensive comparison of Akka Agentic AI and LangChain - features, performance, pros & cons, and when to use each for LLM orchestration and agentic …

Comprehensive comparison of 10 leading open-source AI tools for solo developers - features, performance, pros & cons, and when to use each as …

Comprehensive comparison of LlamaIndex and LangChain - features, performance, pros & cons, and when to use each.

A comprehensive guide to mastering Tunix, a JAX-native library for LLM post-training.

Comprehensive guide on best practices for building and optimizing RAG systems using LLMs.

Guide to using LangChain for LLM orchestration, including core syntax and essential patterns.

Dive deeper into the comprehensive chapters covering all aspects of Teach me LangExtract from absolute beginner to advanced usage, covering …

Learn how to use LangExtract, a Python library for extracting structured data from text using LLMs.

A comprehensive guide to mastering any-llm, Mozilla's unified interface for interacting with various LLM providers.

A comprehensive guide to building intelligent AI agents using LangChain and LangGraph, covering foundational concepts, tool integration, memory …

An in-depth exploration of Large Language Model architectures, focusing on the Transformer mechanism.

A comprehensive guide to Large Language Model (LLM) quantization, covering its principles, various techniques (4-bit, 8-bit, GGUF), practical …

A comprehensive guide to deploying and serving Large Language Models (LLMs) locally, focusing on Ollama for running pre-packaged models, and …

A comprehensive guide to Large Language Model (LLM) pre-training and fine-tuning concepts, covering Supervised Fine-tuning (SFT), Parameter-Efficient …

A comprehensive and practical guide to MLOps and LLMOps principles and practices for managing the lifecycle of Large Language Models and Agentic AI …

A comprehensive and practical guide to Retrieval-Augmented Generation (RAG), covering its core components, document loading, chunking, embedding, …

MTA-Agent introduces a modular, multi-turn agent framework that enhances Multimodal Large Language Models (MLLMs) by integrating specialized tools for …