Context Window
The maximum amount of text (measured in tokens) that an LLM can process at once, including both input and output.
Overview
The context window defines how much conversation history, instructions, and input an LLM can handle in a single request. Understanding and testing context window limits is crucial for multi-turn conversations and long-form interactions.
Context Window Sizes
Common Models:
- GPT-3.5-turbo: 16K tokens (~12,000 words)
- GPT-4: 8K-128K tokens (varies by version)
- Claude 3: 200K tokens (~150,000 words)
- Gemini Pro: 32K-1M tokens (varies by version)
Token Estimates:
- Rough estimate: 1 token ≈ 0.75 words
- 1,000 tokens ≈ 750 words or 2-3 paragraphs
- 10,000 tokens ≈ 7,500 words or ~15 pages
Testing Context Window Limits
Long conversation tests verify your system handles extended multi-turn dialogues gracefully. Create test scenarios with many back-and-forth exchanges that accumulate context over time. Monitor how the system performs as conversations approach and reach window limits. Context retention metrics evaluate whether important information from earlier in conversations remains accessible later. Test if the system can reference facts, preferences, or decisions from early turns after many subsequent exchanges. Long input tests use very large prompts or documents approaching window limits to verify the system handles maximal input without errors or degraded performance.
Context Window Issues
Truncation occurs when conversations exceed the context window, forcing older content to be dropped. This can cause the AI to forget important information from earlier in the conversation, lose track of user preferences or instructions established early on, or behave inconsistently as it loses context. Systems must detect approaching limits and handle them gracefully rather than suddenly losing coherence.
Managing Context Windows
Conversation summarization compresses earlier turns to fit within the window while retaining key information. Periodically summarize completed exchanges into condensed form, preserving critical facts and decisions. Sliding window approaches keep only the most recent turns in full detail while discarding or compressing older exchanges. This works when recent context matters most. Selective retention prioritizes keeping important information like user preferences, key facts, or instructions while discarding less relevant conversational filler.
Testing Strategies
Boundary testing deliberately tests at and near window limits to understand system behavior when approaching capacity. Create conversations that precisely hit limits to see how gracefully degradation occurs. Information persistence testing verifies that critical information established early in conversations remains available throughout, even as the window fills. Test whether the system can still reference and act on early information after many subsequent exchanges.
Best Practices
Design:
- Plan for limits: Design conversations within window constraints
- Summarize when needed: Compress earlier context
- Prioritize recent: Keep latest information
- Test boundaries: Know where system breaks
Testing:
- Long conversations: Test extended multi-turn dialogues
- Memory tests: Verify information retention
- Boundary tests: Test near window limits
- Recovery tests: How system handles overflow
Monitoring:
- Track length: Monitor conversation token counts
- Alert on limits: Warn when approaching window size
- User guidance: Inform users of conversation length limits
- Graceful handling: Degrade gracefully when limit reached