Large Language Models on AI VOID

The World of LLMOps: Why It's Different for Large Language Models

Fri, 20 Mar 2026 00:00:00 +0000

Introduction: The New Frontier of LLMOps

Welcome to the fascinating and rapidly evolving world of LLMOps! If you’re an MLOps engineer, data scientist, or software developer, you’ve likely encountered the incredible potential of Large Language Models (LLMs). From powering sophisticated chatbots to generating creative content, LLMs are transforming how we interact with technology. But moving these powerful models from research labs to robust, scalable, and cost-efficient production systems presents a unique set of challenges.

Unveiling AI Agents: The Next Frontier in Application Development

Fri, 20 Mar 2026 00:00:00 +0000

Unveiling AI Agents: The Next Frontier in Application Development

Welcome, aspiring AI engineers and developers, to an exciting journey into the world of AI agents! If you’ve been experimenting with Large Language Models (LLMs) and marveling at their ability to generate text, answer questions, and even write code, you’re already familiar with a powerful building block. But what if we could empower these LLMs to go beyond single-turn interactions, allowing them to tackle complex, multi-step problems autonomously, just like a human expert would? That’s precisely what AI agents enable, and it’s revolutionizing how we build intelligent applications.

Introduction to JSON and TOON for AI

Sat, 15 Nov 2025 03:00:00 +0000

Introduction to JSON and TOON for AI

Welcome to the exciting world of data formats optimized for Artificial Intelligence! In this introductory chapter, we’ll lay the groundwork for understanding JSON (JavaScript Object Notation) and TOON (Token-Oriented Object Notation), two critical formats for interacting with AI models, especially Large Language Models (LLMs). We’ll explore what they are, why they are so important in the AI landscape, and how to set up your development environment to start working with them.

Introduction to Redis LangCache

Sat, 08 Nov 2025 00:00:00 +0000

1. Introduction to Redis LangCache

Welcome to the exciting world of Redis LangCache! In this chapter, we’ll introduce you to this innovative technology, explain why it’s a game-changer for AI applications, and guide you through setting up your development environment.

1.1 What is Redis LangCache?

Imagine you’re building an AI assistant that answers questions about a product. Users might ask “What are the features of Product X?”, “Tell me about Product X’s capabilities?”, or “List the functionalities of Product X.” All these questions, despite their slight variations, are essentially asking the same thing. Without caching, your AI assistant would send each unique phrasing to an expensive Large Language Model (LLM) every single time, leading to higher costs and slower responses.

Mastering the AI Conversation: Prompt Engineering for Code

Fri, 20 Mar 2026 00:00:00 +0000

Introduction

Welcome back, future-forward developer! In the previous chapters, we explored the landscape of AI coding tools, from interactive copilots to autonomous agents, and how they’re transforming our development workflows. You’ve seen the power of AI to generate code, but have you ever felt like you’re not quite getting the exact output you need? Or that the AI is missing crucial context?

That’s where prompt engineering comes in. Think of it as learning to speak the AI’s language. This isn’t just about typing a question; it’s about crafting precise, contextual, and intentional instructions that guide the AI to deliver highly relevant and accurate results. In this chapter, we’ll turn you into a prompt engineering maestro, capable of coaxing sophisticated solutions from your AI coding partners.

Multimodal LLMs: The Brains of Modern Multimodal AI

Fri, 20 Mar 2026 00:00:00 +0000

Multimodal LLMs: The Brains of Modern Multimodal AI

Welcome back, future AI architects! In previous chapters, we laid the groundwork by understanding how to ingest and represent different types of data—text, images, audio, and video—as numerical embeddings. We learned that the secret to multimodal AI lies in transforming these diverse inputs into a common language that machines can understand. Now, it’s time to introduce the superstar that stitches all these pieces together and makes true cross-modal reasoning possible: Multimodal Large Language Models (MLLMs).

Connecting to AI: Provider Integrations (Ollama, Cloud APIs)

Sun, 17 May 2026 00:00:00 +0000

AI agents, at their core, are problem-solvers that leverage the intelligence of Large Language Models (LLMs). To build truly powerful and versatile AI Packs, your agents need the ability to communicate with these LLMs, whether they’re running locally on your machine or accessible through cloud services. This chapter guides you through the essential process of integrating various AI model providers into your AIPack projects.

Understanding and implementing provider integrations is a critical skill for any AI agent developer. Why does this matter so much? Because it offers immense flexibility and resilience. You can choose local models like Ollama for privacy, cost-effectiveness, and rapid offline iteration. Alternatively, you can leverage cloud APIs (like OpenAI or Anthropic) for their scalability, advanced capabilities, and access to cutting-edge research models. Mastering these integrations allows you to design agents that are performant, adaptable to different operational environments, and aligned with diverse budget constraints.

Foreword

Mon, 01 Jan 0001 00:00:00 +0000

Foreword

The field of artificial intelligence is at a fascinating inflection point. We are moving beyond building models that can simply process information to creating intelligent systems that can reason, plan, and act to achieve complex goals with ambiguous tasks. These “agentic” systems, as this book so aptly describes them, represent the next frontier in AI, and their development is a challenge that excites and inspires us at Google.

“Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems” arrives at the perfect moment to guide us on this journey. The book rightly points out that the power of large language models, the cognitive engines of these agents, must be harnessed with structure and thoughtful design. Just as design patterns revolutionized software engineering by providing a common language and reusable solutions to common problems, the agentic patterns in this book will be foundational for building robust, scalable, and reliable intelligent systems.

RAG System Best Practices: Complete Guide 2026

Sat, 17 Jan 2026 00:00:00 +0000

Introduction

Retrieval-Augmented Generation (RAG) has emerged as a transformative architecture, allowing Large Language Models (LLMs) to access and incorporate external, up-to-date, and domain-specific information. By augmenting prompts with relevant, retrieved context, RAG significantly reduces hallucinations, improves factual accuracy, enhances domain specificity, and enables dynamic knowledge updates without costly model retraining.

Why Best Practices Matter for RAG Systems: Building effective RAG systems is not just about connecting an LLM to a vector database. It involves intricate design choices, particularly concerning the retrieval model, data preparation, and system evaluation. Ignoring best practices can lead to systems that are prone to errors, generate irrelevant or hallucinated content, suffer from poor performance, and are difficult to maintain or scale. The quality of your retrieved context is paramount; as the saying goes, “garbage in, garbage out.” Retrieval errors are consistently identified as the #1 cause of hallucinations in RAG systems.

Building AI Agents in Java with Spring Boot: A Comprehensive Guide

Fri, 03 Oct 2025 00:00:00 +0000

Building AI Agents in Java with Spring Boot: A Comprehensive Guide

Welcome, aspiring AI agent builder! This document is your complete guide to understanding and creating intelligent AI agents using the powerful combination of Java and Spring Boot. Whether you’re entirely new to AI or looking to leverage your Java skills in this exciting field, this guide will take you from the very basics to building sophisticated agentic systems.

We’ll focus on practical, real-world examples using leading Java AI frameworks like Spring AI and Google’s Agent Development Kit (ADK) for Java. By the end, you’ll not only grasp the theory but also have hands-on experience in building agents that can reason, plan, and interact with the world.

Building AI Agents in Java with Spring Boot: A Comprehensive Guide

Fri, 03 Oct 2025 00:00:00 +0000

Building AI Agents in Java with Spring Boot: A Comprehensive Guide