What the Data for This Category Looks Like
Daily documents for feed advertising creative testing include creative scripts, material test sheets, delivery data reports, and iteration lists. Creative scripts record fields such as copy variants and material types. Material test sheets track exposure and click correlation data for each creative group. Delivery data reports update user interaction and conversion metrics daily. Iteration lists synchronize test conclusions weekly. Data sources include advertising platform APIs, internal material libraries, user interaction tracking points, and conversion data. Fields include creative identifiers, material formats, conversion costs, and other related items. There are no fixed statistical percentages. Update cycles primarily follow daily or weekly schedules.
Constraints for Context and Token Workflows
The multi-source mixed data structure of this category requires the context to handle both semi-structured test sheets and unstructured script content simultaneously. It is necessary to balance information relevance and token consumption. The daily or weekly update cycle requires the context to support incremental recall of historical data. This avoids reloading full historical records each time. The large number of creative variables and test volumes requires strict control of total context length. This prevents the model from interrupting generation due to token overflow. Requirements document drafting must link to recent iteration conclusions. The context must retain key test metrics to avoid losing core decision-making basis.
Configuration Settings
This page’s configuration references the interface setup logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers core content of creative scripts, material test sheets, and recent delivery reports required for single-round drafting, and prevents token overflow |
| Context recall count | Top 8–12 historical iteration records | Matches the daily updated test data volume, ensures context relevance while controlling total token consumption |
quoteMaxToken | 2000 token | Limits the reference length of a single historical test report or creative script, and prevents redundant content from occupying context space |
| Context compression ratio | 30%–50% | Compresses multi-source material metadata and repeated delivery data descriptions, and reduces total token usage |
| Maximum tokens per single call | 4000 token | Matches the output length requirement for requirements document drafting, and prevents single requests from exceeding model limits |
Three Common Configuration Errors
- Error scenario: A
[FATAL]prefixed interface request failure log appears when starting the OneAPI container. The root cause is failure to correctly configure the model API key corresponding toACCESS_TOKEN, or inability to access the model service interface in a network-free environment. - Error scenario: The AI-generated requirements document fails to link creative variables to historical test data. The root cause is an overly small
maxContextconfiguration value, which does not recall sufficient iteration lists and delivery data for context reference. - Error scenario: The model’s generated requirements document contains large volumes of repeated material descriptions. The root cause is failure to enable the context compression function, or a compression ratio set outside the reasonable range, which prevents effective filtering of redundant content.
How to Verify Correct Configuration
- Verify the
maxContextandquoteMaxTokenconfiguration values, and confirm they align with the average length range of current business documents. - Confirm that the
ACCESS_TOKENconfiguration item correctly includes the model’s API key. In a network-free environment, check local container logs to confirm interface connectivity. - Launch a single-round requirements document drafting test, and check whether the generated result covers core creative variables and historical test data.
- Review the token consumption of the generated document, and confirm it does not exceed the model’s single-call limit.
The values provided here are common starting points, and should be evaluated 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.