Building Your First RAG System: Embeddings, Chunking, and Vector Databases

Mon, 06 Apr 2026 00:00:00 +0000

Introduction: Beyond the LLM’s Memory

Welcome back, intrepid developer! In our previous chapters, you mastered the art of crafting precise prompts and guiding Large Language Models (LLMs) to perform complex tasks. You’ve seen the power of zero-shot, few-shot, and Chain-of-Thought prompting. But what happens when an LLM needs to answer questions about information it was not trained on, or when its knowledge cutoff means it’s unaware of recent events?

This is where a revolutionary technique called Retrieval-Augmented Generation (RAG) comes into play. RAG empowers LLMs to access and integrate external, up-to-date, and domain-specific information into their responses. Instead of relying solely on their pre-trained knowledge, RAG systems allow LLMs to “look up” relevant facts from a vast external knowledge base before generating an answer. Think of it as giving your LLM an instant, super-fast librarian who can find exactly the right book for any query.

Persistent Agent Memory: Short-Term Context and Long-Term Knowledge Bases

Mon, 06 Apr 2026 00:00:00 +0000

Introduction

Welcome back, fellow AI architect! In previous chapters, we mastered the art of crafting precise prompts and designing agentic workflows. But have you ever noticed that our agents, while brilliant in the moment, sometimes forget what they just said? Or struggle with questions outside their immediate training data? That’s where memory comes in.

This chapter is all about giving our AI agents a memory – both short-term, for coherent conversations, and long-term, for accessing vast knowledge. We’ll dive deep into managing the LLM’s context window, integrating vector databases for external knowledge, and building truly intelligent agents that remember and learn. By the end, you’ll be able to equip your agents with persistent memory, making them far more capable, consistent, and useful in real-world applications.

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Building Your First RAG System: Embeddings, Chunking, and Vector Databases

Introduction: Beyond the LLM’s Memory

Persistent Agent Memory: Short-Term Context and Long-Term Knowledge Bases

Introduction