What the data for this category looks like
The data for this category primarily comes from search ad backends, search term reports, bidding ranking data, and landing page data. Daily produced documents include keyword lists, negative keyword tables, bid records, and search term reports. Fields cover keyword text, matching mode, single bid amount, click volume, conversion volume, and more. Units include yuan, times, and items. Data update frequency is usually daily or weekly. The number of entries per document varies with the scale of the ad account.
What Constraints These Characteristics Impose on the Multi-turn Dialogue and Prompt Engineering Link
Since data sources include multiple types of ad backends and business reports, multi-turn dialogue must clearly distinguish field ownership across different data sources. It must avoid confusing bidding ranking data with landing page conversion data. Document structures are fixed and fields are clearly defined. Prompts must pre-specify required fields such as keywords, matching modes, and bid amounts to ensure generated documents comply with business standards. Data update frequency is high. Dialogue context must be limited to recent daily or weekly data ranges to avoid referencing expired information. A data validation step must be added to the multi-turn workflow to prevent subsequent bidding adjustment errors caused by field mistakes.
How to Configure Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Enable and bind ad business-specific variables | Used to inject category-specific parameters such as real-time bid thresholds, negative keyword rules, and account IDs |
chatId | Bind a dedicated session identifier | Distinguish dialogue contexts for different ad accounts to avoid cross-account data confusion |
messages | Concatenate context in session order | Ensure complete transmission of multi-turn dialogue historical information and avoid loss of key requirements |
| Maximum context length | 800–1200 characters | Matches the typical total entry length of a single keyword list to avoid truncation of core data |
| Single dialogue return limit | Top 10 entries | Adapts the output scale of single-round document drafting and prevents content overload that impairs readability |
| Single request timeout | 300 seconds | Matches the loading pace of ad data and avoids dialogue interruptions caused by overly long data pulling |
| Multi-turn dialogue history retention duration | 600 seconds | Matches the typical work rhythm of ad optimizers and avoids expiration of historical context |
Three Common Mistakes
- Dialogue output only uses Chinese, failing to follow the language requirements of English prompts. This occurs when language enforcement parameters are not bound in
variables, or when prompts do not clearly specify output language rules. - Parsing fails after uploading a keyword list or search term report. This occurs when field mapping rules for the corresponding document are not pre-configured, causing the model to fail to recognize core fields such as keywords and bid amounts.
- Detailed dialogue records cannot be viewed after application invocation. This occurs when dialogue log storage configuration is not enabled, or when
chatIdis not bound to associate sessions with log entries.
How to Verify Successful Configuration
- Upload a test keyword list and check if the generated document includes preset fields and formats.
- Configure English language parameters in
variables, initiate a dialogue with an English prompt, and verify that the output language meets requirements. - Initiate a multi-turn dialogue and generate a session link, check if
chatIdcorrectly associates context data for the current session. - Invoke the application interface and check if the interaction log and parameter call records are fully displayed on the dialogue details page.
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.