Understanding the Limits of What Your AI Can See

Your AI model can only work with what you give it. The context window—the amount of text the model can process at once—determines what “what you give it” actually means.

Pick the wrong context window size, and you’ll either waste money or lose critical information.

What Is a Context Window?

A context window is the maximum amount of text (measured in tokens) that a model can process in a single request.

Tokens aren’t words. Roughly:

• 1 token ≈ 0.75 words
• 1 token ≈ 4 characters

So a 4,000 token context window ≈ 3,000 words ≈ 12,000 characters.

Example:

• GPT-4 Turbo: 128,000 tokens
• Claude 3 Opus: 200,000 tokens
• Llama 2: 4,096 tokens
• GPT-3.5: 4,096 tokens

Everything you send (prompt + documents + history) counts against your context window.

Why Context Window Size Matters

Too small:

• Can’t include full documents
• Can’t maintain conversation history
• Forces you to summarize or chunk data
• Loses context mid-conversation

Too large:

• Costs more (you pay per token)
• Slower processing
• Model gets distracted by irrelevant information
• Harder to control what the model sees

The right size is a balance.

The Cost of Context

Larger context windows cost more:

Using a 200K context window vs. 4K can cost 100x more for the same task if you fill it.

The Performance Trade-Off

Larger context windows have downsides:

1. Slower processing: More tokens = longer inference time
2. Lost focus: Model gets distracted by irrelevant context
3. Hallucinations: More information = more chances to confuse the model
4. Attention degradation: Models perform worse on information in the middle of long contexts

Research shows models pay less attention to information in the middle of long contexts (the “lost in the middle” problem).

When to Use Large Context Windows

Good use cases:

• Full document analysis: Analyze entire contracts, reports, or codebases
• Long conversations: Multi-turn conversations with full history
• Multi-document research: Compare and synthesize across many documents
• Code review: Analyze entire files or modules at once
• Knowledge bases: Include full documentation in a single request

Example: “Analyze this entire 50-page contract and identify all liability clauses.” → Needs large context window to include full document

When to Use Small Context Windows

Good use cases:

• Simple Q&A: Single questions with brief answers
• Streaming responses: Real-time chat applications
• Cost-sensitive tasks: High-volume, low-complexity requests
• Edge deployment: Running models locally or on-device
• Focused prompts: When you control exactly what context is needed

Example: “What’s the capital of France?” → Doesn’t need a large context window

Strategies for Limited Context Windows

If your model has a small context window, use these strategies:

1. Chunking Break documents into smaller pieces, process separately, then combine results.

Document (10,000 tokens) → Chunk 1 (2,000) → Process
                        → Chunk 2 (2,000) → Process
                        → Chunk 3 (2,000) → Process
                        → Combine results

2. Summarization Summarize large documents before including them.

Full document (10,000 tokens) → Summarize → Summary (1,000 tokens) → Process

3. Retrieval (RAG) Only include the most relevant parts of documents.

Query: "What's our return policy?"
Full docs (50,000 tokens) → Retrieve relevant section (500 tokens) → Process

4. Conversation Management Keep only recent conversation history, summarize older messages.

Full history (20 turns) → Keep last 5 turns + summary of first 15 → Process

Strategies for Large Context Windows

If you have a large context window, use it strategically:

1. Include Full Context Put entire documents, full conversation history, and complete examples.

2. Explicit Structure Use clear markers to separate different pieces of context.

<documents>
[Full document 1]
[Full document 2]
</documents>

<conversation_history>
[Full conversation]
</conversation_history>

<task>
[Your actual request]
</task>

3. Prioritize Information Put the most important information first (models pay more attention to the beginning).

4. Reduce Summarization Skip summarization steps—include full information instead.

Choosing the Right Model

For small context windows (≤4K):

• GPT-3.5
• Llama 2
• Mistral
• Cost-sensitive applications

For medium context windows (8K-32K):

• GPT-4
• Claude 3 Sonnet
• Balanced cost and capability

For large context windows (64K+):

• GPT-4 Turbo (128K)
• Claude 3 Opus (200K)
• Gemini 1.5 Pro (1M)
• Document-heavy applications

Real-World Example: Customer Support

Scenario: Build an AI customer support agent.

Small context window (4K):

• Can’t include full customer history
• Can’t include full product documentation
• Must summarize or chunk
• Cheaper per request
• Faster responses

Large context window (128K):

• Include full customer history
• Include full product documentation
• Include company policies
• More expensive per request
• Slower responses
• Better answers

Hybrid approach:

• Use RAG to retrieve relevant docs (not everything)
• Keep last 10 messages of conversation history
• Use medium context window (8-32K)
• Balance cost and quality

The Context Window Checklist

When choosing a model:

• How much context do I actually need?
• What’s my cost budget?
• What’s my latency requirement?
• Can I use RAG instead of large context?
• Should I summarize or chunk?
• Will the model be distracted by extra context?

In Calliope

Context management:

• Chat Studio: Automatically manages context for conversations
• AI Lab: Control context window for custom workflows
• Langflow: Visual nodes for context chunking and retrieval
• Deep Agent: Intelligent context selection for agent tasks

The Bottom Line

• Context window size = cost + capability trade-off
• Larger isn’t always better
• Use RAG to avoid needing huge context windows
• Structure your context to help the model focus
• Choose the right model for your actual needs

Start with a small context window. Upgrade only if you need it.

Pricing Note: The costs and pricing models mentioned in this guide are based on current market rates. LLM pricing changes frequently as models are updated and providers adjust their pricing. Always verify current pricing before making model selection decisions.

Build context-aware AI with Calliope →