What Data for This Category Looks Like
Data for this category comes from engineering orders, sand, gravel, cement and admixture inspection records, aggregate moisture content measurement data, mixing plant control system logs, and project laboratory ledgers. Daily generated documents include mix proportion notices, opening identification forms, production and transport orders, raw material inspection reports, test block retention records, and structural strength reports. Data updates occur per vehicle production batch, per batch of raw material delivery, and daily laboratory ledger updates. Document fields include raw material batch number, material usage per cubic meter, slump index, test block curing age, strength test values, and more. Corresponding units include kilograms, cubic meters, millimeters, days, and megapascals.
Constraints Imposed by These Characteristics on Multi-Turn Dialogue and Prompt Engineering
The data update rhythm for this category is scattered and tightly linked. Multi-turn dialogue must limit the context scope to avoid loading outdated raw material inspection or production batch data. Document fields have clear business unit and format requirements. Prompts must enforce output matching the mixing plant’s standard field content to prevent non-compliant process documents or exception reports. Exception report drafting must link real-time test values such as current batch slump and test block strength. Multi-turn dialogue must retain key context such as raw material batch number and transport time, while filtering irrelevant data from non-current production tasks. Dialogue needs vary across different roles. Context must be used to distinguish the scope of document field usage for roles including quality control, scheduling, and laboratory personnel, ensuring output content aligns with the business requirements of the corresponding role.
Configuration Guidelines
This document is based on FastGPT v4.17.0 configuration specifications.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
messages | Retain the most recent 8 turns of dialogue | Data for this category updates per batch; excessive historical dialogue will introduce raw material data from outdated batches. 8 turns covers the context of the current production task |
variables | Bind current raw material batch number, slump threshold, test block age | Matches the core fields of documents for this category, ensuring prompts can call real-time business data |
| Maximum context length | 4000–6000 characters | Adapts to the content length of single-batch process documents for this category, avoiding truncation of critical business parameters |
| Single recall count | Top 3 matching documents | Documents for this category are categorized by batch; excessive recall will mix up content from different production tasks |
chatId | Bind a unique chatId per production batch | Distinguishes dialogue context across different production batches, preventing cross-batch data confusion |
Three Common Mistakes
- Phenomenon: The same model generates inconsistent process document parameters when called across different workspaces. Cause: Failed to bind exclusive business parameters for the current batch via
variables, causing the model to call default variables from different workspaces. - Phenomenon: Prompts require only use of knowledge base content, but the generated exception report template exceeds the knowledge base scope. Cause: Failed to explicitly limit the exclusive document field scope for this category in the prompt, so the model was not constrained by strict business format requirements.
- Phenomenon: Outdated raw material inspection data from a prior production batch is included in multi-turn dialogue. Cause:
chatIdwas not bound by production batch, and the retention turns ofmessageswere not limited, causing the context to load outdated data.
How to Verify Correct Configuration
- Initiate a multi-turn dialogue for a single production batch, check whether the generated process documents include exclusive business fields for the current batch.
- Switch the
chatIdfor different production batches, confirm that the dialogue context does not introduce historical data across batches. - Adjust the retention turns of
messages, verify that generated content only relates to parameters of the current production task. - Input a general question outside the knowledge base scope for this category, confirm that the model only returns content compliant with business specifications.
The values provided are common starting points and should be evaluated against the user’s own samples.