AI Memory 101 → How Databases Became the Brain of AI Agents

Databases Have Been Here All Along and Now They’re Powering AI Memory

Non-members — read here

Table of Contents

∘ The Fine-Tuned / Prompt Engineering Approach
∘ Vector Databases as Memory (RAG-Based Approach)
∘ Graph Databases and Entity-Based Memory
∘ Hybrid Approaches: Combining Multiple Memory Types
∘ The Traditional Database Approach
∘ Wrapping Up

But apart from making LLMs more intelligent and interactive with data, one problem has refused to go away: memory and context.

The problem is that AI forgets easily. It forgets what you told it five minutes ago, it forgets your preferences, it forgets the “whole point” of the conversation after enough back-and-forth. You could tell it you don’t like coffee, and a few prompts later, it’s recommending espresso. That’s not human-like memory.

This gap sparked a wave of research and experimentation. How do we give AI agents persistent, human-like memory? How do we make them remember key facts about people over long periods and actually use them when making decisions? How do we make talking to AI…