What data for this category looks like
Performance testing and capacity assessment data primarily comes from stress testing platforms, APM monitoring systems, database slow logs, and production traffic baselines. Daily produced documents include stress testing plans, capacity assessment reports, stress testing data records, and optimization suggestion sheets. Common fields include throughput (times/second), average latency (milliseconds), resource utilization (%), peak concurrent count, and others. Data update frequency is monthly or when major versions are released. Document structures typically include modules such as testing objectives, environment configurations, test scripts, indicator thresholds, and conclusions.
What constraints do these characteristics impose for multi-turn dialogue and prompt engineering
Multi-source data requirements mean multi-turn dialogue must integrate information from different channels in sequence. Prompts should guide summarization of basic stress testing data first, then supplement APM bottleneck indicators and slow log details, to avoid information chaos. The non-fixed data update frequency requires prompts to support dynamic adjustment of data ranges, adapting to testing needs for different versions or cycles. The large number of category-specific fields requires prompts to clearly specify which indicators to extract, preventing generated documents from missing core performance parameters. The most time-consuming links of stress testing model design and bottleneck identification require multi-turn dialogue to focus on this scenario, gradually refining the content of requirements and design documents.
How to configure settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the test session ID of the current project | Enable context continuity for multi-turn dialogue, avoid retyping historical data |
messages | Include system prompts + summaries of the most recent 3 stress testing data sets | Ensure dialogue proceeds based on existing performance testing information, reduce redundant input |
variables | Preset lists of throughput, latency, and resource utilization fields | Force generated documents to include category-specific core indicators |
| Maximum context | 8000–12000 tokens | Adapt to integration needs for multi-source documents such as stress testing plans and capacity reports |
| Single history reference | First 8 dialogue records | Filter redundant information, focus on drafting current requirements and design documents |
Three common mistakes
- Symptom: Generated document fragments have inconsistent logic, with conflicting values for the same indicator. Cause:
chatIdis not bound, multi-turn dialogue does not maintain context, and historical data is reset on each call. - Symptom: Generated stress testing plans do not include core performance indicators such as throughput and latency. Cause: Category-specific fields are not bound via
variables, and prompts do not explicitly require extraction of related data. - Symptom: Outputs from multiple AI dialogue nodes executed in batch are mixed, and cannot be matched to specific test scenarios. Cause: Each dialogue node is not configured with an independent
chatId, causing context to be overwritten across nodes.
How to confirm configuration is complete
- Initiate a dialogue for test data summary, then follow up with a question about bottleneck analysis. Check if the dialogue continues the previous discussion to confirm
chatIdbinding is active. - Generate a preliminary stress testing plan document. Check if the document includes preset fields such as throughput and latency to confirm
variablesbinding is correct. - Start two independent test sessions, each sending the same prompt request. Check if outputs match the context of their respective sessions to confirm context isolation works properly.
- View dialogue logs to confirm the number of referenced historical messages matches the set single history reference limit, confirming redundant information has been filtered.
The values provided 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.