What is Context?
Many executives assume that AI “remembers” past interactions the way humans do. In reality, LLMs don’t have long-term memory—they operate within a fixed context window, meaning they can only “see” a limited number of words at a time.
This fundamental concept—context—controls how AI models process, understand, and respond to input. Without it, AI wouldn’t function effectively.
In the world of AI, context refers to the amount of text (tokens) an AI model can “see” at once.
Key Takeaways for Executives:
✅ LLMs don’t have memory—they only process what’s in their context window.
✅ Once a conversation gets too long, earlier parts are “forgotten.”
✅ Different AI models have different context limits (some handle 4K tokens, others 200K+).
✅ For advanced business applications, handling context efficiently is critical.
Instead of asking “Why doesn’t AI remember what I said earlier?”, ask:
👉 “Did my input fit within the AI’s context window?”
What Does “Context” Mean in an AI Model?
Think of an AI’s context window like the field of vision for a camera. If something falls outside the frame, the AI can’t see it—even if it was there earlier.
How Context Works
- AI can only process a fixed number of words (tokens) at a time.
- When new input exceeds this limit, older parts get pushed out and are no longer “seen” by the AI.
- The context window resets every time a new request is sent—AI doesn’t store history across sessions.
💡 AI isn’t forgetting—it just doesn’t have enough space to store past messages indefinitely.
Context Windows: How Much AI Can “Remember”
Different AI models have different context limits, measured in tokens (words, spaces, and punctuation).
| Model | Max Context Window | Example |
|---|---|---|
| GPT-3.5 | 4,096 tokens (≈3,000 words) | Can handle short conversations but loses earlier details. |
| GPT-4-turbo | 128,000 tokens (≈96,000 words) | Can process entire documents at once. |
| Claude 2 | 200,000 tokens (≈150,000 words) | Useful for long business reports. |
💡 The larger the context window, the more information an AI can process in a single request.
What Happens When Context Runs Out?
If you exceed an AI’s context limit, earlier text gets dropped. This is why:
- Long conversations cause AI to “forget” what was said at the beginning.
- Documents that are too long get truncated, leading to incomplete answers.
- AI-generated summaries may miss key points if context is lost.
Example: Context Loss in Action
1️⃣ Beginning of a conversation:
“Our company, Acme Logistics, specializes in supply chain management. We want AI-driven automation.”
2️⃣ 20 messages later:
“What’s the best AI strategy for our business?”
3️⃣ AI response: “AI can help many industries… logistics, healthcare, finance, etc.”
🚨 Problem: AI has lost the detail that Acme specializes in logistics and now gives a generic answer.
✅ Solution: Restate key details in your prompts when conversations get long.
Business Impact: Why Executives Should Care
🔹 AI decisions depend on the data it can “see” in its context window.
🔹 Larger context models are better for handling complex business use cases.
🔹 Self-hosted AI models must be chosen carefully based on their context window size.
Instead of assuming “AI remembers everything,” executives should ask:
👉 “Does my AI model have a large enough context window for my business needs?”
Mastering context-aware AI usage is the first step toward implementing AI in a scalable, effective way.
We’ll cover this in more depth when we begin discussing running AI models on your own hardware and infrastructure.