What the data for this category looks like
The core data for this category comes from employee contact lists, email gateways, internal training platforms, and click statistics systems. Daily produced documents include standardized training courseware, quarterly/annual drill plans, phishing email templates, and drill result reports. Data updates follow a quarterly or annual cycle. Each document includes fields such as drill audience scope, email delivery records, employee click feedback, and rectification suggestions. There is no unified fixed unit, and values adjust based on document type. This category focuses on training and drills for personnel security awareness and behavior. It has clear differences from the security baseline check category, which focuses on system configuration checks.
Constraints on context and token handling
Documents in this category are mostly unstructured training content and semi-structured drill reports. Dispersed data sources make it easy to mix irrelevant fields during context assembly. Quarterly or annual update cycles mean a large volume of historical data must be loaded at one time, which easily exceeds token limits. If fields such as click statistics and audience tags in drill result reports are not filtered, they will occupy excessive context space, causing core drill review content to be truncated. Format requirements for phishing email templates also affect context neatness, increasing uncontrollable token consumption. This document is written based on FastGPT v4.17.0.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-document splicing needs for quarterly/annual summaries, prevents core review content from being truncated |
quoteMaxToken | 1500–2000 characters | Matches single-segment content length of phishing email templates and drill reports, enables precise recall of relevant fragments |
| Single recall count | Top 6 entries | Controls total context token consumption, avoids exceeding limits due to too many non-core data recalls |
| Context filtering rules | Filter by drill cycle and audience tags | Reduces token space occupied by irrelevant data, focuses on core content of current training and drills |
| Content truncation threshold | Calibrated via actual testing | Adapts to drill report data of different scales, prevents loss of critical information |
Three Common Misconfigurations
- Interface display content is truncated, only showing
...[hide 38432 char]. The cause is that a reasonablemaxContextparameter is not set, causing aggregated drill reports and training courseware to exceed the context token limit, leading the system to automatically truncate trailing content. - An error
failed to get gpt-3.5-turbo token encoderoccurs on startup. The cause is that context token encoding rules are not configured correctly, or the referenced external token service has not completed authorization verification. - Question-and-answer response takes too long and token consumption is too high. The cause is that recalled context data is not filtered by drill audience and cycle, leading a large volume of irrelevant historical training data to be included in the context, occupying additional token resources.
How to Verify Configurations
- Import a complete annual drill result report, check that the context preview includes all core fields with no premature truncation.
- Trigger a content summary for a phishing email template, verify that recalled fragments are strongly relevant to the current drill scenario with no irrelevant data mixed in.
- View token consumption statistics, confirm that single-round summary token values fall within the preset reasonable range with no abnormal spikes.
- Simulate an abnormal scenario, such as importing a training courseware of larger-than-normal scale, check that the system triggers reasonable truncation or filtering logic according to configurations.
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.