Context WindowsAI Memory & Token Limits
Context windows define how much text AI can consider at once — its working memory. Understanding this helps you have better conversations and avoid frustrating limitations when the AI "forgets" things.
What Is a Context Window?
What is a context window?
A context window is the AI's "working memory" — the maximum amount of text it can consider at once. Think of it like a desk: you can only fit so many papers on it. Once it's full, you have to remove something to add something new.
What happens when I hit the limit?
When you exceed the context window, old messages get "forgotten." The AI can only see the most recent content that fits. It's like a conversation where someone can only remember the last few minutes.
Is context window the same as memory?
Sort of. The context window IS the AI's memory for your current conversation. But unlike human memory, it doesn't learn or remember between conversations. Each chat starts fresh.
💡 The Desk Analogy: Imagine the AI's context window as a desk with limited space. Every message is a paper on the desk. When it's full and you add something new, the oldest paper falls off and is forgotten.
Context Window Sizes by Model
Different models have different memory limits
GPT-4o
OpenAI
128K
~300 pages
Good for long documents
Claude 3.5 Sonnet
Anthropic
200K
~500 pages
One of the largest
Gemini 1.5 Pro
2M
~5,000 pages
Massive context
Llama 3.1 405B
Meta
128K
~300 pages
Open source option
What Fills Up the Context Window?
Everything in your conversation counts toward the limit
System Prompt
The hidden instructions that define how the AI behaves.
Your Messages
Every message you've sent in the conversation.
AI Responses
Everything the AI has replied with. Often longer than your messages!
Pasted Content
Documents, code, or data you've shared. This can eat up context fast.
Conversation History
All previous exchanges — they stay in context until the window is full.
What Can You Actually Fit?
Real-world examples of context usage
Casual chat
~2,000 tokens
15-20 back-and-forth messages
Code review
~10,000 tokens
500-1000 lines of code + discussion
Document analysis
~50,000 tokens
A small book or lengthy report
Full codebase
~128,000 tokens
Medium project (with 128K model)
Signs You've Hit the Limit
AI "forgets" earlier conversation
You mention something from the start and AI acts confused.
Responses become inconsistent
AI contradicts what it said earlier (because it can't see it anymore).
Error messages about length
Some platforms warn you when approaching or hitting limits.
AI asks for information you already gave
That context got pushed out of the window.
Strategies for Long Conversations
How to work around context limits
Summarize and restart
Ask AI to summarize key points, then start a new conversation with that summary.
EasyBe selective with context
Only paste the relevant code/text, not entire files. Include what matters.
EasyUse larger context models
Switch to Claude (200K) or Gemini (2M) for very long documents.
EasyChunk your content
Process long documents in sections. Analyze Part 1, then Part 2, etc.
MediumRAG (Retrieval-Augmented Generation)
Use embeddings to find and inject only relevant chunks. Technical but powerful.
AdvancedCommon Mistakes to Avoid
Pasting entire codebases
✗ Don't
Dumping all files at once
✓ Do
Only include relevant files and functions
Why: Wastes context on irrelevant code.
Not knowing your model's limit
✗ Don't
Assuming unlimited context
✓ Do
Check the context window size before starting large tasks
Why: Prevents unexpected "forgetting."
Assuming AI remembers everything
✗ Don't
Referencing early conversation late
✓ Do
Re-state important context if the conversation is long
Why: Old context may be gone.
Ignoring output token allocation
✗ Don't
Using all context for input
✓ Do
Leave room for the AI's response (usually 2K-4K tokens)
Why: AI needs space to respond.