Party Building Learning Q&A for Investment Project Approval and Post-Evaluation: Multi-turn Dialogue and Prompt Engineering

Party Building Learning Q&A for Investment Project Approval and Post-Evaluation: Multi-turn Dialogue and Prompt Engineering

What data for this category includes

This category of data is primarily sourced from the State-owned Assets Supervision and Administration Commission investment supervision system, enterprise ERP and project management systems, industry market data, audit and financial statements. Update frequency is quarterly. Daily documents include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset evaluation reports, and post-investment evaluation reports. Document structures cover modules such as project approval processes, financial indicators, implementation progress, and evaluation conclusions. Fields include project codes, total investment amount, implementation cycle, evaluation rating, and corresponding units including ten thousand yuan, year, and point.

Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering

Multiple data sources require multi-turn dialogue to support cross-data source associated queries, to avoid data silos. Quarterly updates require prompts to synchronize latest document versions regularly, ensuring Q&A content aligns with current cycle project information. Diverse, highly professional document types require prompts to distinguish between approval and post-evaluation scenarios, to accurately match corresponding documents. Fields have clear units, so multi-turn dialogue must unify unit expressions, preventing response deviations caused by unit confusion. Errors may lead to state-owned asset losses, so multi-turn dialogue must trace back context to verify data accuracy, preventing incorrect conclusions.

How to set configurations

Configuration ItemRecommended SettingRationale
Maximum context length8000–10000 charactersAdapt to the total length of investment project-related documents, avoid exceeding model processing limits
Question optimization switchEnabled, calibrated based on actual testingOptimize questions for professional terms related to approval and post-evaluation, improve knowledge base retrieval accuracy
Knowledge base retrieval upper limitTop 6 entriesMatch the number of valid documents updated per quarter, avoid redundant information interfering with Q&A logic
Dialogue timeout120 secondsAdapt to the time required for cross-system retrieval of State-owned Assets Supervision and Administration Commission and ERP data
Variable binding ruleBind the project number parameter in variablesAccurately locate full-process data for approval and post-evaluation of the corresponding project
chatId bindingFixed session associated with the current projectEnsure multi-turn dialogue context is not confused, ensure Q&A consistency

Three Common Misconfigurations

  • A 401 No auth credentials found error occurs during dialogue. The cause is failure to correctly configure permission parameters for cross-system retrieval of the State-owned Assets Supervision and Administration Commission system, and failure to bind valid authentication credentials in variables.
  • Multi-turn dialogue context is lost, causing subsequent questions to fail to associate with the current project number. The cause is failure to fix chatId binding to the current session, and failure to retain project context after switching sessions.
  • Project data returned by the dialogue does not match the latest quarterly content. The cause is failure to set automatic update trigger rules for the knowledge base, and use of expired annual investment plan documents.

How to Confirm Configurations Are Properly Set

  • Initiate a single-turn professional question, verify that corresponding project approval or post-evaluation document content can be accurately retrieved, and check that the update time of returned data matches the quarterly cycle.
  • Initiate consecutive multi-turn questions, gradually supplement project details, verify that context is correctly retained, and that chatId remains consistent.
  • Trigger a cross-system data retrieval action, check that project data from ERP can be obtained normally, with no authentication-related errors.
  • Check that the messages parameter in FastGPT v4.17.0 correctly transmits historical dialogue content, ensuring multi-turn dialogue logic takes effect.

The values listed here are common starting points, and should be validated against applicable local 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.