What actually goes inside a context window?

Count the working material, not just your latest question. In Claude's documented request structure, messages, documents, tool results and generated output all contribute to the context window. Anthropic explains what counts.

Three terms worth understanding

Context means the information supplied for the current response. A token is a unit used to count the amount of material the model processes. Context window size is the capacity expressed in those units; Google's guide describes its input limits this way. Google's introduction to long context.

Google uses short term memory as an analogy. Treat that as a useful explanation of limited working space, rather than a claim that a model remembers like a person. The short term memory comparison.

Before attaching files, write down the result you need. Then select the documents that support that result. For an email draft, you might choose the customer's question, the approved offer and the relevant delivery terms.

• Keep the current request explicit.
• Label the approved version of each document.
• Specify the desired answer format.
• Leave unrelated projects out of the task.

If email is your starting point, use the email prompt templates to define the output. A prompt simply means the written instruction you give the assistant.

Why does an AI miss something mentioned earlier?

Separate missing information from missed information. The first means a detail is no longer available in the current context. The second means the model has access to material but fails to use a relevant detail correctly.

Anthropic describes both rolling removal of older material in some chat interfaces and declining accuracy as context grows. These are different problems, even when the reply looks equally forgetful. Context management and declining recall.

Check the constraint, not the assistant's confidence

Instead of asking “Do you remember the budget?”, ask the assistant to state the budget it is using before drafting the plan. Compare that figure with your approved brief. Apply the same check to deadlines, product names and excluded offers.

If you are troubleshooting ChatGPT specifically, do not treat the general explanation as proof of what happened in your chat. The supplied sources do not establish that product's handling of your conversation. Reintroduce the important material and inspect the next answer.

For recurring support work, adapt the customer service prompt templates. Include the approved policy with the task instead of relying on an earlier discussion.

When is a larger context window useful?

Consider more capacity when the task genuinely needs several substantial materials together. Google lists summarizing large collections of text and answering questions over supplied material among long context use cases. Long context applications.

Hypothetical example: a marketing team wants to compare interview transcripts with a campaign brief. Ask it to identify which customer concerns the brief addresses, and require a supporting passage for each match. Keep the original transcripts available for checking.

Capacity does not guarantee complete attention

Anthropic explicitly cautions that more context is not automatically better. Accuracy and recall can decline as the amount of material grows. A document fitting inside the window does not justify accepting every conclusion without checking it. The limits of longer context.

For a short product caption, begin with the relevant product facts and audience. For a comparison across documents, provide the relevant documents together. Let the job determine the material you supply.

If your work involves lengthy reports, the guide to analyzing PDFs with Claude is a useful next step. Carry the same rule into that task: verify important conclusions against the original passage.

How do you keep a long project manageable?

Maintain a short working brief outside the conversation. Use it to record the current state of the job, then review it before starting a new stage. Treat the following structure as a suggested working practice.

1. Objective: state the deliverable you need next.
2. Approved facts: record the product details, dates and terms you have checked.
3. Decisions: distinguish accepted choices from suggestions.
4. Superseded material: name the versions that should no longer be used.
5. Open questions: identify what still needs a human answer.
6. Source material: attach the relevant originals when continuing the work.

A handover prompt you can reuse

Prepare a handover brief for continuing this project in a new conversation. Include the objective, approved facts, accepted decisions, rejected options and unresolved questions. Do not turn a suggestion into an approved decision. Identify the source document for important figures and flag anything that needs my confirmation.

Read that handover before using it. Check names, commercial terms and decisions against your own records. If the brief omits a crucial exception, add it yourself and include the original passage.

When restarting, ask for one specific deliverable. Avoid combining a strategy review, a writing task and an unrelated customer complaint in the same instruction. For planning work, adapt the content planning prompts around your checked brief.

What would this look like in an online shop?

This is a hypothetical example, not a reported customer result. An independent homeware shop is preparing a seasonal email. Its old conversation contains discontinued products, draft offers and several proposed delivery promises.

Build the input before requesting the copy

The owner selects the current product sheet and approved delivery wording. They leave an unconfirmed promotion out of the brief. They then ask for the missing information to be listed before any final email is drafted.

Draft an email using only the approved product sheet and delivery wording below. Audience: existing customers interested in home accessories. Promotion: [approved offer]. Call to action: [chosen action]. If a necessary fact is missing, ask me rather than filling it in. After the draft, list the factual claims I should check.

Review the proposed offer against the brief. Check each product name, availability statement and delivery claim. Remove any assertion you cannot support with the material you supplied.

Keep a simple acceptance record

Mark each requirement as correct, missing or needing revision. If the same requirement is repeatedly missed, rewrite that part of the brief and test again. Record the actual correction effort rather than assuming that a fluent draft is ready to send.

For a team, assign one person to approve the working brief. Ask colleagues to update the approved facts there before requesting another draft. Make the approval responsibility explicit, especially when several people propose different offers.

How should you compare tools and context sizes?

Test the job you actually do. If you are considering ChatGPT, Claude or Gemini, use the same relevant material and the same acceptance checklist. Compare the finished answer, including the work needed to correct it.

For Claude context window figures, check the exact model: Anthropic documents different capacities by model. Do not apply an API capacity figure automatically to a separate chat product. An API is a way software communicates with a service. Claude's model-specific context documentation.

A comparison you can run this week

• Choose a routine task with known correct answers.
• Try the complete relevant material first.
• Try a checked brief with only the necessary source passages.
• Record missing requirements and unsupported statements.
• Compare your review and correction time.

Use a second task before changing your team's routine. Choose one with an exception, such as a delivery rule that applies to only one product. Decide in advance what would make the answer acceptable.

For a broader purchase decision, continue with the Claude versus ChatGPT business guide. Keep your own test results beside any feature comparison.