What the data for this category looks like
Data for defense electronics intelligent due diligence comes from several sources: publicly available official filings related to national defense science and technology, annual reports and temporary announcements of listed defense electronics enterprises, industry update materials released by industry associations, and publicly available qualification documents from military prime contractors.
Data update frequency varies by type:
- Enterprise operation data is updated with annual and quarterly reports
- Industry policies and qualification updates follow official release schedules
- Temporary order and R&D progress data is updated irregularly
Each due diligence document includes structured fields and unstructured attachments. Structured fields cover contractor scope, security classification, contract amount, R&D investment, core team background, and other items. Units include ten thousand yuan, number of personnel, qualification level, and others.
Constraints on multi-turn dialogue and prompt engineering
The scattered data sources, varied update frequencies, and differentiated document structures of defense electronics due diligence data create multiple constraints for multi-turn dialogue and prompt engineering workflows.
First, data is collected across multiple channels including public filings and enterprise announcements. Multi-turn dialogue must gradually guide users to clarify the target subject type and data scope, to avoid invalid cross-subject queries.
Second, some fields involve security qualifications and classified information. Prompt engineering must pre-configure sensitive content filtering rules to prevent the output of unauthorized classified information.
In addition, the coexistence of structured fields and unstructured attachments requires the dialogue workflow to distinguish between structured field queries and attachment parsing requests. It also requires pre-configuring defense electronics industry-specific terminology to ensure consistent understanding of terms.
Finally, differences in update frequencies require the dialogue to proactively prompt users to confirm the data time range, to avoid using outdated information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 preceding characters | Individual defense electronics due diligence documents are lengthy, so sufficient context must be retained to link multiple due diligence materials and industry updates |
systemPrompt | Pre-configured defense electronics industry terminology library + sensitive information filtering rules + clear dialogue workflow guidance | Adapt to defense electronics-specific terminology to avoid misinterpretation, filter classified content, and standardize multi-turn dialogue guidance logic |
retrievalTopK | Top 6–8 results | Defense electronics due diligence data has many fields and strong correlations, so a sufficient number of relevant fragments must be retrieved to cover complete business logic |
similarityThreshold | 0.72–0.78 | Balance precision and recall, adapting to the characteristics of defense electronics data with many professional terms and high differentiation between similar content |
apiTimeout | 120 seconds | Defense electronics data parsing requires processing multiple documents and complex fields, so the timeout period must be extended to avoid task interruptions |
enableSensitiveFilter | Enabled | Defense electronics due diligence data includes sensitive content such as security qualifications, so built-in sensitive content filtering rules must be enabled |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Multi-turn dialogue nodes inserted into the workflow have their results included in the final output. This occurs because output filtering rules for the dialogue node are not configured, causing context from intermediate dialogues to be carried into the final output pipeline.
- Unexpected title content appears in API call conversation logs. This occurs because intermediate process title identifiers are not clearly specified in
systemPrompt, causing the model to generate additional title text that is recorded in logs. - Setting the similarity threshold too high results in insufficient retrieved defense electronics due diligence data fragments, failing to cover complete due diligence logic. This occurs because the configuration does not adapt to the high number of professional terms in the defense electronics industry, incorrectly judging similar industry terms as irrelevant.
How to Verify Proper Configuration
- Initiate a test query containing defense electronics-specific terminology, and check whether the returned result correctly identifies the terms and links to corresponding fields, confirming that the terminology library in
systemPromptis active. - Trigger the multi-turn dialogue node in the workflow, and check whether the final output only includes core due diligence results, confirming that the output filtering rules have been configured.
- Initiate an API call query, and check whether unexpected title content does not appear in the conversation logs, confirming that the format constraints in
systemPromptare active. - Adjust the similarity threshold and initiate a batch query, and check whether the coverage of retrieval results meets business requirements, confirming that the threshold configuration adapts to current data characteristics.
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.