Context Windows in AI: Why Size Matters for Your Prompts

You paste a 40-page document into ChatGPT and ask for analysis. The AI produces a summary, but it seems to have ignored everything after page 25. Or you're having a long conversation, and the AI suddenly forgets details you discussed earlier.

This isn't the AI being forgetful—it's hitting its context window limit. Understanding context windows transforms how you use AI effectively.

What You'll Learn

• What context windows are and why they exist
• Context window sizes across different AI models
• How to tell when you've hit the limit
• Strategies for working with long documents
• Choosing the right model for your needs
• Practical techniques to maximize context usage

What is a Context Window?

Context window = the amount of text an AI can "remember" and consider at once.

Think of it like RAM for AI:

• Small context window = limited short-term memory
• Large context window = can consider more information simultaneously

Measured in tokens:

• 1 token ≈ 4 characters or ¾ of a word
• "Hello world" = ~2 tokens
• 1,000 words ≈ 1,300 tokens
• 100 pages ≈ 75,000-100,000 tokens

Context window includes:

• System instructions
• Your entire conversation history
• Your current prompt
• The AI's responses
• Any files or documents you've shared

Context Window Sizes: Model Comparison

GPT-4 Family

GPT-4 Turbo (latest):

• Context: 128,000 tokens (~100 pages or 96,000 words)
• Output: Up to 4,096 tokens
• Best for: Long documents, extensive conversations

GPT-4 (original):

• Context: 8,192 tokens (~6 pages)
• Extended: 32,768 tokens (~25 pages)
• Output: Up to 4,096 tokens

GPT-3.5 Turbo:

• Context: 16,384 tokens (~12 pages)
• Output: Up to 4,096 tokens
• Best for: Quick tasks, cost-sensitive applications

Claude Family

Claude 3 Opus/Sonnet/Haiku:

• Context: 200,000 tokens (~150 pages or 150,000 words)
• Output: Up to 4,096 tokens
• Best for: Extremely long documents, entire books

Google Gemini

Gemini 1.5 Pro:

• Context: 1,000,000 tokens (~750 pages)
• Output: Up to 8,192 tokens
• Best for: Multiple long documents, full codebases

Gemini 1.5 Flash:

• Context: 1,000,000 tokens
• Output: Up to 8,192 tokens
• Best for: Fast processing of large documents

Visual Comparison

Context Window Sizes (in pages):

GPT-3.5 Turbo    |████████░░░░░░░░░░░░░░░░░░░░░░░░| 12 pages
GPT-4 Original   |███████░░░░░░░░░░░░░░░░░░░░░░░░░| 6 pages  
GPT-4 Extended   |███████████████████░░░░░░░░░░░░░| 25 pages
GPT-4 Turbo      |████████████████████████████████| 100 pages
Claude 3         |████████████████████████████████| 150 pages
Gemini 1.5 Pro   |████████████████████████████████| 750 pages

Why Context Windows Matter

Problem 1: Mid-Document Amnesia

What happens:

You: "Summarize this 150-page contract"
[Paste entire contract into GPT-4 Original]

AI: [Reads first 6 pages, then...]
"Based on the contract section I can see..."
[Ignores pages 7-150]

Why: Document exceeds 8K token context window

Solution: Use Claude 3 or Gemini (larger windows)

Problem 2: Conversation Memory Loss

What happens:

Turn 1: "I'm planning a wedding for 200 guests in June"
[... 30 turns of conversation ...]
Turn 31: "What was the guest count again?"
AI: "I don't have information about guest count in our conversation"

Why: Early conversation has been pushed out of context window

Solution: Periodically summarize and restate key facts

Problem 3: Incomplete Analysis

What happens: You ask AI to review code with 50 files, but it only catches issues in the first few files.

Why: Entire codebase exceeds context window

Solution: Analyze files in batches or use model with larger context

How to Tell You've Hit the Limit

Signs You're Approaching the Limit

🚩 AI ignores later parts of documents

You: "What does page 80 say about liability?"
AI: "I don't see information about liability in the document provided"
[Even though it's clearly on page 80]

🚩 AI forgets earlier conversation

Turn 1: You mention your role is "Product Manager"
Turn 50: AI asks "What's your current role?"

🚩 Responses become vague

AI: "Based on the portions of the document I can analyze..."
AI: "From what I can see in the available context..."

🚩 AI truncates long outputs

Check Token Usage

In API calls:

1response = openai.ChatCompletion.create(
2    model="gpt-4",
3    messages=messages
4)
5
6# Check token usage
7tokens_used = response['usage']['total_tokens']
8print(f"Tokens used: {tokens_used} / 8192")

Rough estimation:

Words in input × 1.3 = approximate tokens
100 pages × 750 tokens/page = 75,000 tokens

Strategies for Long Documents

Strategy 1: Choose the Right Model

Document analysis:

• < 10 pages → GPT-4 Turbo works fine
• 10-100 pages → GPT-4 Turbo, Claude 3
• 100-700 pages → Claude 3, Gemini 1.5 Pro

Cost consideration:

• GPT-4 Turbo: Most expensive per token
• Claude 3: Mid-range pricing
• Gemini: Often most cost-effective for huge documents

Strategy 2: Chunk and Summarize

For very long documents, process in stages:

Step 1: Chunk

Step 2: Summarize each chunk

Prompt: "Summarize this 30-page section, focusing on [key topic]"
Save summary for each section

Step 3: Analyze summaries

Prompt: "Based on these 10 section summaries, analyze [question]"
Paste all summaries (much shorter than full text)

Example workflow:

1# Pseudo-code for document analysis
2sections = split_document(doc, pages_per_section=25)
3
4summaries = []
5for section in sections:
6    summary = ai.summarize(section)
7    summaries.append(summary)
8
9# Now analyze summaries (fits in context)
10final_analysis = ai.analyze(summaries, question="Risk assessment")

Strategy 3: Extract Before Analysis

Don't paste entire documents—extract relevant sections first:

Bad approach:

Paste 100-page employee handbook
"What's the vacation policy?"

Good approach:

Search handbook for "vacation" sections (using Ctrl+F)
Paste only relevant 2-3 pages
"Explain this vacation policy"

Tools for extraction:

• PDF text search
• grep for code files
• Document outline/TOC for targeted sections

Strategy 4: Reference-Based Prompting

Let AI know it's working with a partial view:

Vague:

"Analyze this contract for risks"
[Paste 80 pages]

Specific:

"This is pages 1-50 of a 200-page contract. Analyze these sections 
for financial risks. I'll provide pages 51-100 in the next prompt 
for operational risks."

Benefits:

• AI knows it's not seeing everything
• You get targeted analysis per section
• Can combine insights after multiple passes

Strategy 5: Use Embeddings and Vector Search

For massive documents (books, entire codebases):

How it works:

1. Split document into smaller chunks
2. Create embeddings (vector representations) for each chunk
3. When user asks question, find most relevant chunks
4. Send only relevant chunks to AI for analysis

Tools:

• LangChain (Python library)
• LlamaIndex
• Pinecone, Weaviate (vector databases)

Use case: Company knowledge base, legal document library, codebase analysis

Maximizing Context Efficiency

Technique 1: Compress Information

Instead of full text:

Employee 1: Sarah Johnson, hired 2020, dept: Marketing, 
salary: $75k, performance: Excellent, location: NYC

Employee 2: Mike Chen, hired 2019, dept: Engineering, 
salary: $95k, performance: Good, location: SF

[... 100 more employees with full details ...]

Use structured format:

ID | Name | Hire | Dept | Salary | Perf | Location
1 | Sarah Johnson | 2020 | Mktg | 75k | Exc | NYC
2 | Mike Chen | 2019 | Eng | 95k | Good | SF
[...]

Saves 50-70% tokens while preserving information.

Technique 2: Remove Redundancy

Before:

The company was founded in 2010. In 2010, the founders started 
with just 3 employees. By 2015, which was 5 years after founding 
in 2010, the company had grown to 50 employees...

After:

Technique 3: Clear Old Context

For long conversations, periodically reset:

"Let's start fresh. Here's a summary of what we've discussed:
- [Key point 1]
- [Key point 2]
- [Key point 3]

Moving forward, let's focus on [new topic]"

This clears old tokens while preserving essential context.

Technique 4: Use System Messages Wisely

System messages count toward context limit:

Wasteful system message:

"You are a helpful assistant. You should always be polite, 
professional, thorough, accurate, clear, concise, and you should 
format your responses nicely using markdown. Remember to always 
cite sources and double-check facts before responding..."
[200 tokens of instructions]

Efficient system message:

"You are a data analyst. Format responses as markdown tables."
[15 tokens]

Save tokens for actual content.

Practical Examples

Example 1: Contract Review

Challenge: 150-page merger agreement

Solution:

1. Use Claude 3 (200K context)
2. Or: Extract key sections (financials, liability, termination)
3. Analyze each section separately
4. Synthesize findings

Prompt 1: "Review pages 1-50 (corporate structure and overview) 
for compliance issues"

Prompt 2: "Review pages 51-100 (financial terms) for unfavorable 
conditions"

Prompt 3: "Review pages 101-150 (liability and termination) for 
risk factors"

Final: "Based on these three analyses: [paste summaries], what are 
the top 5 concerns?"

Example 2: Codebase Understanding

Challenge: 100-file Python project

Solution with smaller context:

1. Start with architecture overview (README, main.py)
2. Ask about specific modules
3. Deep dive into problem areas

Turn 1: [Paste README + main.py]
"Explain the overall architecture"

Turn 2: [Paste specific module]
"How does authentication work in auth.py?"

Turn 3: [Paste related files]
"How do auth.py and user.py interact?"

Solution with large context (Gemini):

[Paste entire codebase - 100 files]
"Identify all security vulnerabilities and explain the auth flow"

Example 3: Research Paper Analysis

Challenge: Analyze 20 research papers

Solution:

Step 1: Get summary of each paper
- Paste paper 1: "Summarize methodology and findings"
- Save summary
- Repeat for all 20 papers

Step 2: Comparative analysis
- Paste all 20 summaries
- "Compare methodologies and identify gaps in research"

Choosing the Right Model

Decision tree:

Do you need to process more than 100 pages at once?
├─ Yes → Claude 3 or Gemini 1.5 Pro
└─ No → Continue

Do you need the most accurate responses?
├─ Yes → GPT-4 Turbo
└─ No → Continue

Is cost a major concern?
├─ Yes → GPT-3.5 Turbo or Gemini
└─ No → GPT-4 Turbo

Are you processing code?
├─ Yes → Claude 3 (excellent code understanding)
└─ No → GPT-4 Turbo (general purpose)

Future: Infinite Context?

Current research directions:

Recurrent models: Process unlimited length by "remembering" summaries of previous chunks

Retrieval augmentation: Fetch relevant info from database instead of holding everything in context

Long-context training: Models trained specifically for 1M+ token contexts

For now: Plan around current limits, choose appropriate models

Key Takeaways

• Context window = how much text AI can consider at once
• Measured in tokens: ~1.3 tokens per word
• Varies by model: 8K (GPT-4 original) to 1M (Gemini 1.5)
• Includes everything: prompts, responses, conversation history
• Hit the limit? AI ignores parts of input or forgets earlier context
• Solutions: Choose larger model, chunk documents, extract relevant sections
• Efficiency matters: Compress information, remove redundancy

Conclusion

Context windows are the invisible constraint on AI capabilities. Understanding them transforms frustrating "why isn't this working?" moments into strategic decisions about model selection and prompt structure.

For everyday tasks, GPT-4 Turbo's 128K tokens suffice. For document analysis, Claude 3's 200K tokens handle most needs. For truly massive documents, Gemini's 1M tokens enable what was previously impossible.

Know your limits. Work within them. Choose the right tool. Your AI usage just became dramatically more effective.

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