Context Windows Explained: Why Token Limits Matter

This article is written in English. Our training modules are available in French.

Ever had an AI "forget" something you told it just a few messages ago? That's the context window at work—and understanding it changes how you interact with AI.

What Is a Context Window?

A context window is the maximum amount of text an AI model can "see" at once. Think of it as the AI's working memory—everything it can consider when generating a response.

The Reading Window Analogy

Imagine reading a book through a small window that only shows 2 pages at a time:

[Page 1-2 visible] → You can reference what's in view
[Page 3+] → You've "forgotten" earlier content

That's exactly how LLMs work. They can only process what fits in their window.

Context Window Sizes (2025)

Different models have vastly different capacities:

| Model | Context Window | Approximate Words | |-------|---------------|-------------------| | GPT-3.5 | 4K tokens | ~3,000 words | | GPT-4 | 8K-128K tokens | 6K-96K words | | GPT-4 Turbo | 128K tokens | ~96,000 words | | Claude 3.5 Sonnet | 200K tokens | ~150,000 words | | Gemini 1.5 Pro | 1M+ tokens | ~750,000 words |

Note: 1 token ≈ 0.75 words in English, ~0.5 words in French

What Counts Against Your Context?

Everything in the conversation uses tokens:

1. System Instructions

"You are a helpful assistant specialized in legal documents..."
→ Uses tokens from your window

2. Conversation History

User: [Previous question] → Tokens
AI: [Previous response] → Tokens
User: [Current question] → Tokens

3. Retrieved Documents (RAG)

[Document chunk 1] → Tokens
[Document chunk 2] → Tokens
[Document chunk 3] → Tokens

4. The Response Being Generated

The AI's answer → Also uses tokens!

Key insight: A 128K context window doesn't mean 128K for your documents. System prompts, history, and the response all compete for space.

Why Context Windows Matter

1. Memory Loss

When conversations exceed the window, early messages get "pushed out":

Message 1: "My name is Alex"    ← Eventually forgotten
Message 2: "I work in HR"       ← Eventually forgotten
...
Message 50: "What's my name?"
AI: "I don't have that information" 😕

2. Document Limitations

You can't just paste an entire book and ask questions:

❌ "Here's a 500-page manual. Summarize it."
   → Exceeds context window
   
✅ "Here are the relevant sections. Summarize them."
   → Fits in context

3. Cost Implications

More tokens = higher API costs:

Input: 1,000 tokens × $0.01/1K = $0.01
Input: 100,000 tokens × $0.01/1K = $1.00

The same question can cost 100× more depending on context size.

Strategies for Working Within Limits

1. Summarize History

Instead of keeping full conversation history:

❌ Keep all 50 messages verbatim

✅ Summarize: "Previous discussion covered:
   - User is Alex from HR
   - Looking for vacation policy info
   - Already reviewed section 3.2"

2. Chunk Documents Smartly

Break large documents into retrievable pieces:

Full document: 50,000 tokens (won't fit)
↓
Chunk 1: 500 tokens (relevant section)
Chunk 2: 500 tokens (relevant section)
↓
Only retrieve what's needed

3. Use Focused Prompts

Ask specific questions rather than broad ones:

❌ "Tell me everything about this contract"

✅ "What are the termination clauses in section 4?"

4. Leverage System Prompts Wisely

Keep system instructions concise but complete:

❌ 2,000 token system prompt with examples
   → Less room for actual content

✅ 200 token focused system prompt
   → More room for documents/history

The Context Window Trade-Off

| Large Context | Small Context | |--------------|---------------| | ✅ More memory | ✅ Faster responses | | ✅ More documents | ✅ Lower cost | | ❌ Higher latency | ❌ More forgetting | | ❌ Higher cost | ❌ Harder to manage |

There's no "best" size—it depends on your use case.

Common Mistakes

1. Assuming Unlimited Memory

"But I told you my preferences 20 messages ago!"
→ That's likely outside the window now

2. Ignoring Token Costs

Sending 100K tokens for a simple question
→ Expensive and slow

3. Not Planning for Growth

System works great with 10 documents
→ Breaks when scaled to 1,000 documents

Key Takeaways

1. →Context window = AI's working memory limit
2. →Everything competes for space: prompts, history, documents, output
3. →Larger windows (128K+) exist but have cost and speed trade-offs
4. →Smart strategies: summarize, chunk, focus
5. →Understanding limits helps you design better AI interactions

Ready to Master Context?

This article covered the what and why of context windows. But production AI systems require sophisticated strategies for context management.

In our Module 9 — Context Engineering, you'll learn:

• →The WRITE, SELECT, COMPRESS, ISOLATE framework
• →Dynamic context window management
• →Chunking strategies for RAG systems
• →Memory persistence patterns
• →Production optimization techniques

→ Explore Module 9: Context Engineering