What this category of data looks like
Metric caliber data is sourced from internal business accounting systems, regulatory reporting platforms, and official statistical ledgers of financial institutions. Two update schedules apply. Mandatory regulatory indicators are updated synchronously on a quarterly or annual basis. Internal operating indicators are updated on demand or monthly. The structure of each individual data document is fixed. It includes fields including metric code, standard name, official definition, calculation logic, statistical cycle, applicable subject, and unit of measurement. Most units of measurement use fixed formats such as currency units, percentages, and person-times. No dynamically changing auxiliary fields are included.
Constraints Imposed on Multi-turn Conversation and Prompt Engineering
The fixed field structure of metric caliber data requires multi-turn conversations to accurately target core fields such as metric code and statistical cycle. This avoids matching deviations caused by vague retrieval. Indicators with different update schedules must have their current version cycle clearly specified at the start of a conversation. This prevents calling outdated data. Complex calculation logic requires multi-round follow-up questions to supplement statistical dimensions. For example, distinguishing year-over-year, month-over-month, or cumulative values. It is also necessary to enforce unit of measurement verification in prompt templates. This prevents unit confusion issues.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | Previous 10 conversation turns + 3500 characters | Multi-turn conversations for metric caliber data must retain historical dimension follow-up records. This avoids repeated confirmation of information such as statistical cycle and applicable subject. |
recall_top_k | Top 6 results | Metric caliber data has a fixed structure. Too many recalled results increase context redundancy. Too few may miss precise matching items. |
similarity_threshold | 0.82–0.88 | Matching accuracy for metric names and codes is relatively high. A threshold that is too low introduces irrelevant metrics. A threshold that is too high may fail to match approximate expressions. |
rerank_top_k | Top 3 results | Metric caliber data has strong uniqueness. Retaining the top 3 results after reranking covers precise matching results. |
maxConversationTurns | 8 turns | Dimension follow-up questions for metric caliber data typically require no more than 8 turns to clarify complete query conditions. Too many turns cause context overflow. |
prompt_template | Verify in the order of "metric name/code + statistical cycle + unit of measurement". Ask sequentially for any missing fields | Metric field structure is fixed. Guiding queries in a fixed order quickly clarifies query conditions and reduces invalid conversations. |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: Returned metric data does not match the requested statistical cycle. Cause: The current indicator update version is not forcibly bound in the prompt template. This leads to calling outdated quarterly or annual caliber data.
- Symptom: The unit of measurement for an indicator is repeatedly asked during multi-turn conversation. Cause: Historical unit confirmation information is not retained in the
maxContextconfiguration. This causes context window overflow or loss of key records. - Symptom: A 500 error is triggered during retrieval after importing metric caliber documents. Cause: The documents contain unescaped special characters such as nested parentheses and percent signs. No preprocessing filtering is performed during the document parsing stage. This triggers parsing failure.
How to Confirm Proper Configuration
- Initiate a test conversation with a clear metric code, statistical cycle, and unit of measurement. Check that returned results match the preset metric caliber data.
- Initiate a multi-turn follow-up test. For example, first ask for the metric name, then add statistical dimensions. Check that the conversation retains historical information and gradually clarifies complete query conditions.
- Import a batch of structured metric caliber documents. Trigger the retrieval process. Confirm that no parsing errors or service errors occur.
- Adjust the similarity threshold range. Test matching effects across different thresholds. Confirm that matching accuracy meets business requirements.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.