TL;DR
AI has no memory. Context is all it can see. Give it the right Context, and it’s brilliant. Give it the wrong one, and it’s clueless. Mastering Context is the key to working effectively with AI.
By now, most of us have used some kind of AI chatbot—whether it’s ChatGPT, Claude, or whatever AI assistant your company just rolled out. And you’ve probably noticed something strange: it’s clearly smart, yet it keeps doing dumb things.
For example, you set some ground rules at the start of a conversation, and halfway through, it forgets them. Or you explain your background once, and next time you chat, you have to explain it all over again.
Even the most powerful models in 2025—GPT-5, Claude 4.5, Gemini 3—still have this problem. To understand why, we need to look at how language models actually interact with us.
Context: The Starting Point and Boundary of Every Conversation
Once a language model is trained, its capabilities and knowledge are essentially locked in. Everything you type during a conversation—that’s not part of its training. We call this Context.
Here’s the simplest way to put it: Context is everything the AI can see in the current conversation.
This includes:
- Your chat history with it
- System settings (the hidden instructions you don’t see—like when the platform secretly tells it “you are a polite assistant”)
- Any documents or data you paste in
Add all of that up, and you get the Context.
Think of it like hiring a brilliant new employee who knows nothing about you. Every time you assign them a task, you have to explain your company background, project status, and personal preferences from scratch. Context is essentially the briefing you hand them—without it, even the smartest person won’t know how to help you.
Here’s the catch: every language model has a limited Context capacity. Some can handle more, some less—basically, there’s a limit to how much text it can “see” at once. And every time you start a new conversation, the model doesn’t remember anything from before. It’s a blank slate. Every single time.
Why Does AI Get Dumber the Longer You Talk?
This isn’t just your imagination.
Think of AI like an intern you’re giving verbal instructions to. If you tell them to do 20 steps in a row, and they mishear a few along the way, the final result is going to be off. AI works the same way.
Research has shown that AI makes small errors at each step of a task. Say there’s a 5% error rate per action—sounds low, right? But errors compound:
| Conversation Turns | Success Rate |
|---|---|
| 5 turns | 77.4% |
| 10 turns | 59.9% |
| 20 turns | 35.8% |
| 50 turns | 7.7% |
The more steps, the more things go sideways. And this doesn’t even account for what happens when the Context window fills up and the model starts “forgetting” earlier parts of the conversation.
To be fair, this mainly affects complex, multi-step tasks. If you’re just chatting casually, you probably won’t notice the errors. But if you’re writing code, doing analysis, or working through logic problems, one wrong step can derail everything.
That’s why AI seems sharp at the start of a conversation but feels dumber after an hour or two. It’s not actually getting dumber—the Context is getting too long and noisy, and errors are piling up.
How Do Platforms Make AI “Remember” You?
You might feel like ChatGPT or Claude remembers things about you from previous conversations.
But here’s the truth: the model itself has zero long-term memory—like a goldfish, it starts fresh every single time.
So why does it feel like it remembers? Because the platform is secretly slipping it a cheat sheet:
- Summarized history: The platform condenses your past conversations into a summary and injects it at the start of each new chat
- Dynamic retrieval: When you ask a question, the platform quietly searches your old data and feeds relevant bits to the model
The reality is: AI doesn’t actually remember you. It’s just reading a condensed version of your history with it every time.
This “memory” is an illusion—a clever one, but still an illusion. And here’s the thing: these “memories” also take up Context space.
Why Controlling Context Is Everything
Once you understand what Context is, something becomes clear: how precisely you control Context determines how well AI performs.
In the way language models work, the more relevant the Context is to the task, the better the output. The less relevant, the worse. So if you want AI to perform at its best, the key is: how do you provide high-quality Context?
In 2025, Anthropic (the company behind Claude) proposed a shift in thinking: we should move from “Prompt Engineering” to “Context Engineering.”
What’s the difference?
- Old mindset (Prompt Engineering): “How should I phrase this instruction?”
- New mindset (Context Engineering): “What Context configuration will most likely get the model to produce what I want?”
Here’s a cooking analogy:
- The old approach: “Let me teach you step-by-step how to make this dish.”
- The new approach: “Here are all the ingredients and my taste preferences—figure out the best way to cook it.”
This shift matters. We used to focus on how to ask. Now it’s more about how to inform.
What Makes Good Context?
Anthropic offers a precise definition: Find the smallest but most relevant set of information to maximize the desired outcome.
In plain English: Give information that’s precise, relevant, and free of fluff.
More Context isn’t always better. Stuff it with irrelevant information, and the model gets distracted and loses focus. It’s like handing your employee a briefing packed with unrelated company history, last year’s project notes, and office gossip—they won’t know what actually matters.
Good Context should be:
- Highly relevant to the current task
- Free of noise
- Complete with the key information needed to do the job
- Clearly structured so the model can parse it easily
Real Example: Same Question, Different Context
Let’s look at an example:
No Context:
“Write me an email.”
→ AI gives you a generic, boilerplate email. Nothing specific.
Basic Context:
“Write me an email to a client we’ve worked with for three years. They just got a new manager. Keep it formal but warm.”
→ Completely different result. At least it’s targeted.
Full Context:
On top of the above, you also provide:
- Basic info about the client
- Past email exchanges with them
- The purpose and background of this email
- Your company’s history with theirs
→ The output quality jumps another level.
The difference? The quality of Context.
If you don’t want to go that far, at least remember this simple formula:
Who’s the audience + What’s the purpose + What tone to strike
Just clarify these three things, and your results will be way better than a bare “write me an email.”
From “Teaching AI How to Do Things” to “Giving AI Enough Information”
Earlier, I mentioned the shift from Prompt Engineering to Context Engineering. Another way to look at it: we’re moving from “teaching AI how to do things” to “giving AI enough information to figure it out.”
Back when language models weren’t as capable, our prompts were mostly instructions—telling AI what steps to follow. AI was like a newbie who needed hand-holding.
Now, with 2025-level models, things are different. They’re smart enough to know how to do things. Our job is to provide enough relevant information so they can produce great output.
Anthropic observed something interesting internally: in just one year, the percentage of engineers using AI jumped from 28% to 59%, and self-reported productivity gains increased significantly. What changed their work wasn’t the model getting smarter—it was people learning how to feed it the right Context.
Conclusion
Understanding Context is the first step to working effectively with AI.
Once you realize that Context is all AI can see, you start asking different questions: How do I put the right information in? How do I make sure it sees what it needs to see? How do I avoid stuffing it with noise?
Next time AI seems to get dumber, try this mindset:
Think about what information to give before thinking about what instruction to give.
Instead of jumping straight to “what command should I type,” ask yourself: “If this were a new hire helping me, what background, data, and constraints would I tell them?” Write that down—and you’re doing Context Engineering.
In future posts, we’ll dive deeper into how to control Context effectively. This discipline is called Context Engineering.