What the Data for This Category Looks Like
Data related to delivery effect optimization primarily comes from ad platform backends, attribution systems, CRM lead databases, and landing page data. Regularly produced documents include daily/weekly delivery reports, budget adjustment records, material A/B test sheets, and optimization review reports. Data updates occur on a daily or real-time basis. Data fields include ad spend, conversion volume, customer acquisition cost, material click-through rate, lead source, and more. There is no unified fixed format, and adjustments are made flexibly based on business changes.
What Constraints Do These Characteristics Impose on the Context and Token Link
Mixed input of ad delivery-related documents and real-time data leads to large fluctuations in context length. High-frequency updates of real-time data require context to include the latest adjustment records and daily conversion data. Long-cycle review reports, meanwhile, occupy significant token quotas. Differences in fields across different documents require context recall to precisely match business scenarios, preventing irrelevant data from being included and causing invalid token consumption. Core decisions for delivery effect optimization rely on real-time metrics, so delays in context recall directly impact decision efficiency. As such, context and token configurations must balance data timeliness and quota rationality.
How to Set the Configurations
Configuration settings are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 10%–20% above total business document length | Reserve additional data requirements for troubleshooting abnormal metrics |
quoteMaxToken | 3000–5000 token | Adapt to the content length of single material test sheets or adjustment records |
| Single context recall count | Top 5 entries | Match the batch content volume of daily delivery documents |
| Context recall update interval | 10–15 minutes | Adapt to the daily or real-time update rhythm of real-time data |
| Global context token limit | 8000–12000 token | Cover mixed input of multiple daily reports, review reports, and real-time data |
| Context data filtering threshold | Calibrated via actual testing | Exclude irrelevant historical data to reduce invalid token consumption |
Three Common Mistakes
- Token consumption per conversation far exceeds daily usage. Background logs show multiple irrelevant historical review reports were recalled. The cause is failure to configure reasonable context filtering rules and recall counts, leading to non-current-day historical data being incorrectly mixed into the context and wasting token quotas. Additionally, failure to correctly set the reserved space for
maxContextfurther worsens invalid consumption. - Model output text is truncated unexpectedly, or the interface prompts a context token limit exceeded error. The cause is failure to adjust
maxContextand the global context token limit configurations based on the actual total length of delivery documents. The default quota is insufficient to handle mixed input of multiple daily reports and test sheets. - Unable to adjust the model's output token limit, with the default upper limit only 4000 token. The cause is failure to correctly modify
quoteMaxTokenor global context configuration items, failing to adapt to the long document and data recall requirements of delivery effect optimization.
How to Confirm Proper Configuration
- Review context recall logs to verify that recalled data is delivery-related documents required for the current business scenario, with no irrelevant historical data mixed in.
- Input a complete delivery daily report, adjustment record, and material test sheet to confirm no token limit exceeded errors are triggered.
- Compare single-conversation token consumption data before and after configuration adjustments to confirm consumption is within a reasonable range.
- Verify the completeness of model output to confirm no unexpected truncation occurs.
The values provided here are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.