This is the first in  a series of eight articles meant to help take the mystery out of Artificial Intelligence.  I’ve created easy to follow explanations and illustrations of the most critical things to know about Generateive AI via Large Language Models.

Imagine that you’re three emails deep with your company’s new AI assistant. You asked it to recommend a training course. It suggested leadership fundamentals. You took the course. Now you’re back asking, “What should I do next?”

The AI asks what course you completed.

Wait—weren’t you *just* talking about this?

Here’s what’s actually happening.

AI systems don’t “remember” conversations the way you think they do. When you talk to an AI, you’re not adding new information to its brain. Either you are sending it just that one message and it loses all memory of previous conversational context, or you’re sending your entire conversation history back to it, every single time.

Imagine if your brain worked this way: Every morning you wake up, someone hands you a diary, and you have to re-read everything that happened yesterday to know who you are. That’s how most AI works today. It doesn’t store memories. It re-reads messages.

Key Vocabulary

Stateless Architecture: AI that treats each conversation as independent, with no memory between sessions. Every interaction requires sending the complete conversation history back to the AI for it to have context.

Stateful Architecture: AI that maintains continuous understanding by storing structured information about past interactions—not raw transcripts, but organized facts and relationships.

Context Window: The temporary “reading space” an AI has available for processing information in a single interaction—typically around 200,000 words (roughly two novels). Once exceeded, earlier information gets cut off.

Conversation Transcript: The raw record of every message exchanged between user and AI. In stateless systems, this entire transcript must be re-sent with each new message.

This creates an illusion. The AI *seems* to remember because developers keep feeding it the transcript. But it’s not memory—it’s memorization on demand.  In a purely stateless system you could do something like – “Hey, i have a puppy named Cat” – the LLM will likely respond something about that being clever or ironic. Probably it will even repeat your puppy’s name. Now ask the AI what your puppy is named. If the model is completely stateless – it will say something like, I don’t know anything about your puppy. If so, that indicates that your AI is not doing anything at all to retain the state – remember what has been discussed previously.

Why does this matter?

Because there’s a limit to how much transcript an AI can re-read. Most systems cap out around 200,000 words—roughly two novels. Once you exceed that, they start forgetting the beginning. Not because they’re forgetful, but because there’s literally no room left in their temporary reading space.

This is called stateless architecture. Every interaction is independent. The AI doesn’t retain anything between sessions unless someone explicitly stores and re-sends the transcript.

Real memory works differently. In my experiements, creating ai enhanced systems, I’ve had to create separate memory architectures. The AI doesn’t naturally “remember” that a user visited yesterday. I write infrastructure that stores that fact in a database, searches for relevant past interactions, and injects them into the conversation as context.

This isn’t about making AI smarter. It’s about building systems around the AI so it can *act* like it remembers, even though it technically doesn’t.

The better approach is called stateful architecture. These systems maintain continuous understanding by storing structured information—not just raw transcripts, but facts. “Marcus completed cybersecurity training on March 15. He’s working on the client portal project. He asked about advanced modules.”

Stateful systems can answer: “Based on your recent work with client security, here are three advanced modules that build on what you learned in March.”

Stateless systems can only answer: “What training have you completed?”

Key Takeaways

1. Most AI doesn’t remember—it re-reads. Each conversation requires sending the entire history back to the AI. This is expensive and fragile as conversations grow.

2. Context windows have hard limits. Around 200,000 words, the AI starts forgetting earlier parts of the conversation—not from memory failure, but from architectural constraints.

3. True memory requires separate infrastructure. Stateful systems store structured facts in databases and retrieve relevant information on demand, rather than replaying entire transcripts.

4. The difference compounds at scale. For organizations managing thousands of personalized learning journeys, stateless AI becomes exponentially more expensive and less reliable than purpose-built stateful architectures.

The difference compounds over time. If you’re trying to guide 5,000 employees through personalized learning paths, stateless AI becomes expensive and fragile. Every conversation requires retrieving, formatting, and re-sending mountains of context. Stateful AI stores structured facts once and retrieves only what matters.

Here’s the uncomfortable truth: Most companies selling “AI-powered” tools are just wrapping stateless models with clever prompts. They’re building expensive diary-readers, not memory systems.

When you’re evaluating AI tools, ask:
– Does your system remember our past interactions?
– Can it track progress over weeks and months?
– Or does it need me to repeat context every time?

The answer tells you whether you’re buying infrastructure or illusion.