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Context Windows

How to Avoid Context Overflow: 5 Context Management Patterns

When building conversational agents or processing large documents, context window capacity is a hard ceiling. Exceeding the limit results in API errors that crash your application. This guide explains 5 production-tested patterns to manage context and avoid overflows.

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1. The Sliding Window History Pattern

Store the full conversation in a database, but send only the last N messages (e.g. the last 10 messages) in the active API prompt. This places a strict cap on token costs and ensures context length remains stable.

2. Dynamic Summary Compression

Track your token usage. When conversation history exceeds 50% of the model's context limit, run an asynchronous task that summarizes the oldest messages into a brief summary paragraph, clearing space for new conversation.

3. MapReduce Context Partitioning

For massive document analysis, do not send the entire file at once. Split the document into small chunks, summarize each chunk individually, and then run a final prompt to synthesize the individual summaries into a master report.

Frequently Asked Questions

How do I detect context limits in code?

Tokenize your prompts locally using Tiktoken before calling the API. If the token count exceeds your safe threshold (e.g. 90% of model limit), trigger compression routines.

Does sliding window make the model forget?

Yes. The model will not remember details from messages that fall outside the active window, unless you use a summarization fallback.