#rag (40)
- Designing Memory and State Management for Autonomous AI Agents
Learn to design and implement memory and state management systems that enable autonomous AI agents to achieve persistent understanding and robust, long-running workflows.
- 8 Search APIs for Agentic AI RAG Systems: Complete Comparison 2026
Comprehensive comparison of top search APIs for agentic AI RAG systems in 2026 - features, performance, pros & cons, and when to use each for optimal agent performance.
- Context Control and Large Codebases: Managing Agent Memory
Master context control in AIPack to manage AI agent memory effectively, especially when working with large codebases. Learn RAG, chunking, and dynamic context loading.
- Prompt Engineering and Agentic AI for Production
Learn to build and deploy advanced AI applications using prompt engineering and agentic AI workflows, focusing on practical, production-ready techniques for developers.
- Build Your First RAG System Using Embeddings and Vector Databases
Readers will build a RAG system, integrating document chunking, text embeddings, and vector databases to enhance LLM knowledge with external data.
- Introduction to Retrieval-Augmented Generation (RAG)
Grasp Retrieval-Augmented Generation fundamentals, see its value for production LLM applications, and build a functional RAG system using Python.
- Build AI Agents with Persistent Short-Term and Long-Term Memory
Learn to equip AI agents with persistent short-term context and long-term knowledge, making them more capable, consistent, and useful in applications.
- Understanding AI Agent Memory Systems: A Practical Guide
Explore the essential role of memory in AI agents, covering different memory types, storage, retrieval, and how agents use them to learn and maintain context.
- Design and Optimize Context for Large Language Models
Master context engineering to design, structure, and optimize input for Large Language Models, enhancing their performance and output quality.
- Modern AI Engineering: Core Concepts & Emerging Topics (2026)
A structured overview of the most important and trending AI engineering topics in 2026, covering agent systems, context engineering, infrastructure, and modern AI workflows.
- Advanced RAG Techniques for Building Intelligent AI Systems
Learn to implement advanced Retrieval-Augmented Generation techniques like hybrid search and GraphRAG to build highly accurate and robust AI applications.
- Equip AI Agents with Long-Term Memory Using Agentic RAG
You will build a Retrieval-Augmented Generation system using vector databases, enabling AI agents to access external knowledge and reduce hallucinations.
- Advanced Concepts & Best Practices for Production-Ready Memory Systems
Explore advanced concepts and best practices for designing and implementing robust, scalable, and secure memory systems for AI agents in production environments.
- Build a RAG Agent with Memory for Contextual AI Responses
You will learn to build a RAG agent that uses memory and conversational history to provide accurate, context-aware AI responses.
- AI Agent Long-Term Memory: Episodic & Semantic Foundations
Readers will understand how AI agents utilize episodic and semantic memory to store past experiences and general knowledge, enabling complex, adaptive behaviors.
- Vector Memory and Embeddings: The Power of Similarity
Explore vector memory and embeddings, understanding how AI agents leverage numerical representations for efficient similarity-based information retrieval and enhanced contextual understanding.
- Storing Agent Memories: From Files to Databases and Vector Stores
Explore how AI agents store their memories, from simple file systems to advanced vector databases, understanding the trade-offs and practical implementations.
- Retrieving AI Agent Memories for Contextual Awareness
Learn how AI agents retrieve information from memory using various strategies to achieve contextual awareness and overcome LLM limitations.
- Detecting & Mitigating Hallucinations in Generative AI
Learn how to detect and mitigate AI hallucinations in generative models like LLMs, ensuring reliability and trustworthiness in production systems.
- Architecting AI Systems with LLMs, Generative AI, and Agents
Learn to design and integrate scalable, trustworthy AI systems that leverage Large Language Models, Generative AI, and multi-agent orchestration patterns.
- Beyond the Prompt: Building Multi-Source Context Pipelines (RAG)
Explore Retrieval-Augmented Generation (RAG) to overcome LLM limitations, integrate external knowledge, and build dynamic, multi-source context pipelines for production AI systems.
- Production-Ready Context: Best Practices & LLMOps
Master production-ready context management for LLMs. Learn best practices for designing, structuring, and optimizing context within LLMOps workflows to enhance AI reliability and performance.
- Breaking Down Information: Smart Chunking Strategies
Master smart chunking strategies to effectively break down large documents for LLMs, improving context management, relevance, and RAG system performance.
- Build an End-to-End Production RAG System with LLMOps
Build a robust, scalable, and cost-efficient Retrieval Augmented Generation system using LLMOps best practices for real-world production.
- Build Multimodal RAG Systems with Diverse Data Sources
Learn to implement Multimodal RAG, integrating diverse data types to enhance AI knowledge bases and overcome Large Language Model limitations.
- Move Beyond Simple RAG Chunking with Advanced Context Assembly
Learn to implement advanced context assembly techniques to overcome simple chunking limitations and build highly coherent contexts for RAG systems.
- Implementing Advanced Embeddings and Hybrid Search for RAG 2.0
Apply advanced embedding models and hybrid search strategies to improve retrieval accuracy and relevance for LLMs, enhancing RAG 2.0 systems.
- LLM Planning for Agentic Retrieval in RAG 2.0
Learn to implement agentic retrieval systems where LLMs plan and execute complex information retrieval tasks, advancing your RAG 2.0 capabilities.
- Retrieval-Augmented Generation: Basics, Architecture, Limitations
Learn the core architecture and functionality of basic Retrieval-Augmented Generation and identify its key limitations for AI systems.
- Building with GraphRAG: N-Hop Expansion and Practical Integration
Dive deep into GraphRAG, learning how to build knowledge graphs, perform N-hop expansion, and integrate graph-based retrieval into your RAG 2.0 systems for enhanced accuracy and multi-hop reasoning.
- GraphRAG: Model Relationships for Better LLM Context
Discover how GraphRAG leverages knowledge graphs to model complex relationships, enhancing retrieval for multi-hop reasoning and richer LLM context.
- Advanced RAG: LLM Query Rewriting and Multi-Hop Retrieval
Discover how LLMs enhance RAG retrieval, rewriting complex user queries and orchestrating multi-step searches to find more accurate information.
- Modern RAG 2.0: Advanced Retrieval Guide
Dive deep into modern RAG 2.0, exploring advanced techniques like hybrid search, GraphRAG, and multi-hop retrieval. Learn to overcome basic RAG limitations for superior accuracy and relevance.
- Deploying RAG 2.0: Best Practices, Evaluation, and Real-World Projects
Explore best practices for deploying RAG 2.0 systems, learn crucial evaluation methodologies, and discover real-world applications to build robust and accurate Generative AI solutions.
- LlamaIndex vs LangChain: Complete Comparison 2026
Comprehensive comparison of LlamaIndex and LangChain - features, performance, pros & cons, and when to use each.
- Best Practices for Designing and Optimizing RAG Systems
Implement best practices for designing and optimizing RAG systems, ensuring your AI applications deliver accurate, reliable, and performant results.
- Enhance LLMs with Custom Data using Retrieval-Augmented Generation
After this chapter, you will implement a RAG system to enhance large language models with external, up-to-date, and domain-specific knowledge.
- Memory & State Management for Persistent AI Interactions
Learn how to implement memory and state management in AI applications using LangChain, Vector Stores, and RAG.
- Build a Smart AI Research Assistant Agent Project
Design and implement a multi-agent AI system to build a smart research assistant that searches, synthesizes, and summarizes information.
- Guided Project 2: Optimizing a RAG Application with LangCache
Learn how to optimize a RAG application using LangCache for faster response times and reduced costs.