Multi-turn Dialogue and Prompt Engineering for Infrastructure Construction Marketing Content

Relevant data for infrastructure construction marketing targeting the financial industry comes from project bidding documents, construction logs

Data Profile for This Category

Relevant data for infrastructure construction marketing targeting the financial industry comes from project bidding documents, construction logs, material ledgers, as-built drawings, and client communication correspondence. The data update rhythm shifts with project phases: updates are concentrated during project initiation and bidding stages. Weekly progress updates and monthly cost ledger updates occur during construction. Data is archived after project completion. Document formats include structured Excel spreadsheets, long-form Word construction plans, and PDF bidding documents. Fields include project number, material model, shift unit price, and construction area. Units include cubic meters, square meters, work shifts, and ten thousand yuan, along with other engineering-specific units.

Constraints for Multi-turn Dialogue and Prompt Engineering

The long-text and structured batch nature of infrastructure construction data imposes several constraints. Multi-turn dialogue systems must support chunked loading and segmented parsing of large files to avoid single-context overflow. Multi-turn dialogue must retain contextual memory for exclusive fields such as project number, material model, and construction area to prevent cross-turn confusion. Frequently updated phased data requires dialogue flows to support real-time retrieval of the latest ledgers, without relying on static knowledge base caches. Frequent use of engineering-specific terminology requires prompts to pre-set engineering domain term mapping rules, to reduce understanding deviation. Cross-project multi-turn dialogue must distinguish exclusive parameters of different projects to avoid context interference.

Configuration Settings

Configuration KeyRecommended ValueRationale
maxContext8000–12000 charactersAdapts to the need for retaining long context such as project numbers and multiple material parameters in infrastructure construction multi-turn dialogue, avoiding truncation of critical business information
UPLOAD_FILE_MAX_SIZE2000 MBSupports upload of 100,000-character Word documents and 15,000-row Excel spreadsheets, adapting to bulk import of infrastructure construction business data
PARSE_FILE_TIMEOUT_SECONDS600 secondsMeets parsing time requirements for large Excel ledgers and long PDF bidding documents, avoiding mid-parsing interruptions
Number of Retrieved EntriesTop 6–8 entriesCovers multiple infrastructure construction business scenarios including construction plans, material ledgers, and progress records, balancing context volume and retrieval efficiency
Similarity Threshold0.75–0.85Filters non-engineering irrelevant knowledge base content, adapting to the presence of near-synonymous expressions in engineering terms, improving dialogue matching accuracy
system_promptPre-set engineering term mapping rules, clarify the reference logic of exclusive fields such as project number and shift unit priceUnifies semantic understanding standards for infrastructure construction-specific terms, reducing ambiguity probability in multi-turn dialogue

The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

  • Phenomenon: A 403 Forbidden error or CORS cross-origin error is returned when calling the /api/v1/chat/completions interface. Cause: Cross-origin whitelist for the private deployment environment is not configured, causing front-end requests to be blocked by the browser.
  • Phenomenon: AI dialogue output takes more than 30 seconds after knowledge base search is completed. Cause: The maxContext or number of retrieved entries parameters are not adjusted, resulting in excessive context loading volume, or knowledge base retrieval optimization configuration is not enabled.
  • Phenomenon: The dialogue process fails to load complete data after uploading a 100,000-character Word document or 15,000-row Excel spreadsheet. Cause: The UPLOAD_FILE_MAX_SIZE and PARSE_FILE_TIMEOUT_SECONDS parameters are not increased, resulting in file truncation or parsing timeout.

How to Verify Correct Configuration

  • Upload a single 100,000-character Word document and 15,000-row Excel spreadsheet. Verify that upload and parsing processes have no errors, and confirm that configuration items are effective.
  • Initiate a multi-turn dialogue containing exclusive fields such as project number and material model. Verify that contextual memory is coherent and no field confusion occurs, and confirm that context and prompt configurations are reasonable.
  • Call the dialogue interface, check the returned token consumption field. Verify that token calculation logic is normal, and confirm that relevant configurations after private deployment are correct.
  • Initiate a query containing engineering-specific terms. Verify that AI replies accurately match term definitions, and confirm that prompt configurations meet 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.