What This Category of Data Looks Like
Data sources for this category include regulatory and rule repositories, higher-tier legal databases, departmental policy document repositories, and administrative normative document filing systems. Update frequency is irregular. Daily processed documents include normative document drafts, drafting explanatory statements, legality review opinions, solicited comment summaries, and filing reports. Document structure primarily uses clause-based text, with core fields such as main document content, drafting basis, and solicited comment status. Individual document length varies widely, ranging from hundreds of characters to tens of thousands of characters.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Dispersed data sources and irregular update cycles require multi-turn dialogue to gradually guide users to clarify the version of the current review document and its associated basis. Complex document structures with specialized legal wording require prompts to clearly distinguish the hierarchical relationship between higher-tier legal basis and local policies, to avoid confusion in applicable rules. Wide variation in individual document length requires multi-turn dialogue to dynamically adjust context length, to avoid exceeding model processing limits. Review requires comparison across multiple types of basis, so prompts must guide the model to break down tasks step-by-step according to review stages, to ensure coverage of core review points such as authority, procedure, and legality of provisions.
Configuration Settings
The following configurations are developed for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Maximum Context | 8000–12000 token | Adapts to the total length of normative document drafts and associated basis, to avoid truncation of core review content |
| Timeout Setting | 600 seconds | Adapts to processing duration for long document reviews, to avoid task interruption due to large volumes of review content |
| Question Optimization | Enabled, triggered per dialogue turn | Optimizes user questions for specialized legal wording, to improve model understanding accuracy of review requirements |
| Single Search Limit | Top 8 results | Matches retrieval needs across multiple types of basis, to cover common higher-tier legal and local policy entries |
| Overlap Length | 200–300 characters | Ensures contextual coherence of chunked text, to avoid splitting legal clauses |
variables | Preset review stage variables (e.g., authority basis, procedural requirements) | Fixes core review rules, to reduce repeated verification across multi-turn dialogue |
chatId | Bound to unique identifier of current review task | Ensures correct contextual association for multi-turn dialogue, to avoid content confusion across different review tasks |
Three Common Configuration Mistakes
- Phenomenon: Knowledge base responses are truncated, returning only partial review opinion content. Cause: Maximum context length is not properly set, exceeding model processing limits leading to truncation of core basis.
- Phenomenon: A 500 error is returned when importing a normative document draft. Cause: Long text structure of the document is not accommodated, and chunking configuration does not cover large clause sections, leading to parsing failure.
- Phenomenon: All historical results are output after multiple AI dialogue modules run in a workflow. Cause: The
variablesparameter is not configured to retain only output results from the current review stage, and no result filtering rules are set.
How to Confirm Proper Configuration
- Upload a typical normative document draft, check that chunked text fully covers all clauses with no splitting.
- Initiate one round of review dialogue, confirm that the model can correctly associate the uploaded document with preset higher-tier legal basis.
- Test multi-round progressive questioning, such as first asking about authority compliance then procedural compliance, confirm that context is coherent with no content loss.
- Check
messagesparameter logs, confirm that context for each dialogue turn only includes content related to the current review task.
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 2026-09-28.