What the data for this category looks like
Data sources for engineering consulting financing daily reports include project financing ledgers from engineering consulting institutions, loan disbursement receipts from cooperative banks, and project filing information from local development and reform commissions. The update schedule is full synchronization of the previous day’s data every early morning. Each daily report document is split by individual project. Core fields include project number, financing entity name, current loan amount, arrival date, approval progress, and supporting consulting service content. The unit for monetary amounts is ten thousand yuan. Date fields uniformly use the YYYY-MM-DD format, with no additional nested sub-documents.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
The daily full-update nature of engineering consulting financing daily reports requires limiting multi-turn dialogue context window sizes, to avoid redundant data occupying token quotas. Fields contain engineering consulting-specific terminology, such as supporting consulting service content. Prompts must embed domain terminology specifications to ensure the AI accurately identifies the association between projects and services. The split structure by project requires multi-turn dialogue to support filtering historical context by project number, to avoid cross-project data interfering with dialogue logic. The strong binding attribute between arrival date and loan amount requires prompts to explicitly require the AI to retain field matching results from the previous round, to ensure subsequent questions can reuse already identified project information.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 tokens | Matches the daily full-update daily report data volume, covers the project context for multi-turn dialogue, and avoids token overflow |
systemPrompt | Please answer questions based on the project fields of engineering consulting financing daily reports (project number, financing entity, loan amount, arrival date, approval progress), prioritize matching the context of the specified project number, and retain the field results identified in the previous round for reuse in subsequent questions | Aligns with the field structure and multi-turn reuse requirements of engineering consulting financing daily reports, and clarifies the AI’s response rules |
recallTopK | Top 3–5 entries | Each daily report is split by project; a small number of recalls can cover the context of the target project, and avoid mixing irrelevant data |
similarityThreshold | 0.75–0.85 | Accurately matches the relevance between project numbers and queries, and filters financing daily report data from irrelevant projects |
historyCleanupStrategy | Retain the most recent 10 turns of dialogue per conversation ID | Only retains recent context related to the current session, avoiding accumulation of historical data that affects dialogue efficiency |
promptTemplate | {question}, please answer using the engineering consulting financing daily report data in {context} | Explicitly binds the query and the recalled context data, ensuring the AI uses the correct data source |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common misconfigurations
- Phenomenon: Non-target project financing daily report data is mixed into the dialogue history, leading to deviations in responses. Cause: No rules for filtering context by project number are configured, and the matching scope of
recallTopKis not limited. - Phenomenon: Historical records for a specified session cannot be cleaned individually, and all dialogue content must be manually deleted. Cause: Session-level cleanup configuration for
historyCleanupStrategyis not enabled, and the default cleanup strategy is global cleanup. - Phenomenon: Subsequent questions cannot reuse the project number or loan amount results identified in the previous round, requiring repeated queries for the same information. Cause: No requirement to retain the previous round’s field matching results is specified in
systemPrompt, and no context reuse rules are defined in the prompt.
How to confirm the configuration is correct
- Initiate a query that includes a specified project number, check whether the recalled context data only contains the financing daily report information for that project, and confirm that the context filtering configuration is effective.
- Initiate consecutive multi-turn queries, verify whether the loan amount or arrival date identified in the previous round is automatically reused, and confirm that the context retention configuration is effective.
- View the token consumption of the dialogue history, confirm that it does not exceed the set range of
maxContext, and verify that the context window configuration is reasonable. - Adjust the value of
similarityThreshold, test the number of recall results under different thresholds, and confirm that the 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.