Requirements and Design Document Drafting for Search Ad Keyword Management: Context and Token

Typical users of search ad keyword management include SEM optimizers, agency operations staff, and brand marketing personnel. Daily produced and used

What the Data for This Category Looks Like

Typical users of search ad keyword management include SEM optimizers, agency operations staff, and brand marketing personnel. Daily produced and used documents include keyword lists, negative keyword tables, bid records, and search term reports. Data primarily comes from search ad backends, search term reports, bidding ranking data, and landing page data. Most updates occur on a daily or weekly basis. Core documents for this category include fields such as keyword text, match type, bid amount, and search volume. Weekly search term report data volumes are typically large, requiring categorized management by document type.

Constraints on Context and Token

Scattered data sources and differing update rhythms create constraints for context and token handling. Context must adapt to both short-field keyword lists and long-text search term reports. Token usage must be differentiated by document type. Daily updated bidding data must be included in context in real time. Weekly search term reports require controlling total token count per load to avoid exceeding model limits. Additional time-consuming steps such as keyword expansion and negative keyword screening process large numbers of entries. Context token allocation must balance recall accuracy and window capacity. Truncation of context due to overly long single data entries can reduce the accuracy of document drafting.

How to Set Configurations

Configuration ItemRecommended ValueRationale
maxContext8000–12000 tokenMatches the total estimated token count of weekly search term reports and bid records, and adapts to the context window upper limit of FastGPT v4.17.0
quoteMaxToken1500 tokenLimits the token length of a single referenced negative keyword table or keyword record, preventing valid information from being truncated due to overly long single content
Context chunk overlap length200 charactersMatches the field interval between keywords and bid records in keyword lists, preventing associated data from being lost after chunking
Single context recall countTop 6 entriesBalances the recall accuracy and token usage of bidding ranking data, ensuring core data is prioritized for inclusion in context
Context refresh frequencyUpdated dailyMatches the daily update rhythm of search ad backend data, ensuring context always uses the latest bid and keyword information

Three Common Misconfigurations

  • A "Reached the max retries" error appears during calls. This occurs when the maxContext refresh interval is not adjusted to match the update frequency of search ad data. Expired context data triggers repeated retry loading attempts.
  • An ACCESS_TOKEN invalid prompt appears in the configuration interface. This happens when the search ad backend API key is not correctly entered into the corresponding configuration item, preventing loading of ad data sources.
  • Token consumption exceeds expectations during multi-model calls in workflows. This occurs when token usage across ad documents is not differentiated. Context is not split by category such as keyword lists and negative keyword tables, leading to repeated loading of identical data.

How to Confirm Configurations Are Properly Set

  • Import a single week's search term report file. Check that no truncation prompt appears after context loading, and all core fields are fully displayed.
  • View the maxContext configuration item value. Confirm it matches the token estimate for the weekly data, with no significant overage or underage.
  • Trigger a complete keyword expansion process. Check that no "Reached the max retries" type errors appear.
  • Randomly select a negative keyword record. Confirm the referenced content is complete, with no critical information truncated.

The values provided 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.