Tokens Pro RSS: budget a feed request deliberately

Budget the task as a whole

Choose an operating budget below the verified limit of your selected model and integration. Reserve space for stable instructions, source labels, any conversation context, and the response you need. The remaining amount is the working allowance for article material. Keep this calculation visible in your processing configuration.

For illustration, a project could assign 12,000 tokens to a request, reserve 2,000 for instructions and context, and allow 1,000 for its answer. That leaves 9,000 for selected source material. These are planning numbers for an example, not the limits or prices of a particular model.

Select before compressing

Rank candidate entries by their usefulness to the question. Remove duplicates and irrelevant announcements first. Then consider whether the remaining task needs full articles, summaries, or specific passages. A detailed explanation of one relevant release may be more useful than shallow coverage of a dozen unrelated updates.

Use the tokenizer appropriate to your model to measure the final prepared input. The OpenAI token-counting reference gives examples of encoding and counting text. Recount after adding source labels, output instructions, or examples; these additions are part of the request too.

Define what happens when content exceeds the budget

1. Check whether repeated or irrelevant entries can be removed.
2. Select the passages that directly support the requested comparison.
3. Split independent questions into separate bounded requests.
4. Preserve qualifications and source labels in every batch.
5. Combine reviewed findings using their original identifiers.

Avoid cutting text at an arbitrary character position. A truncation can remove the exception that changes the meaning of an entire paragraph. When a passage is incomplete, record that limitation in the preparation stage and decide whether the task can still be answered from the available evidence.

Track measured usage alongside the number of entries processed and the quality of the result. Large usage changes can reveal a repeated footer, a newly expanded source, or a preparation error. Compare like-for-like tasks before deciding that a different prompt or model is responsible.

Keep current model limits and any applicable pricing in a separate, reviewed configuration record. Your editorial selection policy can remain stable as those details change. Continue with text preparation when formatting noise is consuming the budget.