Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteIf a prompt exceeds an AI model’s context limit, first confirm which limit you hit, then reduce or restructure the request. A context window is a token budget for the full interaction—not just the text you typed—and it can include files, tool instructions, the requested answer, and sometimes reasoning. The exact limit and what happens at overflow depend on the model, version, and app or API.
First, identify which limit you hit
A context-window error is only one possible reason a request may fail or produce an incomplete answer. A model may also have a separate maximum-output cap, an API request-size limit, a file-size restriction, or a consumer app usage limit. Check the exact model and product documentation, then compare it with the error message. An app’s limits and controls may differ from those of the provider’s API.
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Overflow behavior is not universal. OpenAI says an oversized prompt risks a truncated output. Anthropic documents a 400 invalid_request_error when the input alone exceeds the window. For Claude 4.5 and later, input plus the requested maximum output can exceed the window and still be accepted, but generation may stop with model_context_window_exceeded. Google warns that a response may overlook provided content or miss connections. These are provider-specific descriptions, not rules that apply to every model. OpenAI conversation-state guidance, Anthropic context-window documentation, and Google Gemini Apps Help describe their respective products.
The Tool Desk
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Tokens are the units models use to process text; they do not correspond exactly to characters or words. A request’s total can also include conversation history, tool definitions, structured formatting such as a schema, files, and images. A plain-text count may therefore understate the size of an API request.
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Use the counting method for the provider and endpoint you are using. OpenAI recommends its complete-input counting API for Responses inputs, and Anthropic documents a token-counting API. Leave room for the answer you want; where the system uses reasoning tokens, those may also draw on the available budget. See OpenAI’s token guide and Anthropic’s context-window documentation.
Try these fixes in order
- Remove what does not affect the answer. Delete repeated instructions, duplicated source passages, irrelevant chat history, and examples that do not change the result. OpenAI specifically recommends shortening or rephrasing a prompt and removing unnecessary or repeated context. OpenAI token guide
- Narrow the question. Ask for one decision or result at a time, and state the format you need. For example, ask for the key differences between two sections rather than a summary of an entire archive.
- Split long material into coherent sections. Send one section at a time and ask the same focused question of each. Then provide the section-level results for synthesis. Keep source names or section labels with each result so the final answer can be traced back to the right material.
- Summarize before continuing. Ask for a concise carry-forward summary, preserving exact names, dates, definitions, constraints, and references that matter to the next step. Start a new conversation with that summary if the old history has accumulated too much context.
- For a long API conversation, use provider-specific context management if available. Anthropic documents server-side compaction, which summarizes older context, and context-editing strategies such as clearing old tool results. OpenAI points API users to context compaction features. Availability and implementation depend on the provider and model. Anthropic context-window documentation and OpenAI conversation-state guidance
Choose an approach for large documents or collections
| Approach | Best fit | Main trade-off |
|---|---|---|
| Manual chunking | A document can be divided into meaningful sections and questions can be handled section by section. | The final synthesis depends on preserving important details across section answers. |
| Summarization or compaction | A continuing task needs older conversation or source material condensed to make room. | A summary may omit a detail needed later; retain exact facts and constraints that must carry forward. |
| Retrieval-augmented generation | A question concerns selected passages from a large collection rather than every item at once. | Only retrieved material is supplied, so relevant passages can be missed. |
| Context caching | The same long material will be reused in supported Gemini API workflows. | Caching reuses uploaded material; it does not make irrelevant context useful or remove the need to fit the request within applicable limits. |
| A larger-context model | The task genuinely requires more source material to be considered together. | A larger window does not guarantee attention to every detail and may increase latency; compare accuracy and cost for the actual workload. |
Google’s Gemini API guidance describes summarization, sliding-window techniques, retrieval, and context caching for long-context tasks. It also advises that, in most cases—especially when total context is long—the query or question should come after the other context. Treat that as guidance for the Gemini API, not a universal prompt rule. Google Gemini API long-context documentation
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why a bigger context window may not solve it
A larger window can avoid splitting a source that needs to be considered as a whole, but capacity is not the same as reliable attention. Google warns that exceeding a context window can lead to missed content or connections; Anthropic notes that recall and accuracy may degrade as token count grows. Long requests can also increase latency. Use the larger window only when the task benefits from seeing more material together, and check performance on the task itself. Google Gemini API long-context documentation and Anthropic context-window documentation
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Best Value
Check the result after retrying
- Confirm the model or product accepted the complete request rather than silently omitting a file or earlier history.
- For a multi-part analysis, verify that the final synthesis includes conclusions and constraints from every relevant section.
- If the response looks incomplete, reduce the material further, ask a narrower question, or use a workflow that retrieves or summarizes relevant passages.
- Check the current documentation before relying on a particular capacity or overflow behavior; limits change and can differ between app and API deployments.
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