What the data for this category looks like
Relevant data for pharmaceutical preparations production comes from approved formulation processes, MES systems, electronic batch records, equipment and environmental monitoring data, LIMS laboratory management systems, and quality management systems. Daily documents include master production files, batch production records, batch packaging records, cleaning clearance records, inspection records, deviation and CAPA documents, and batch release review forms. Data is updated for each production batch, critical process step, and monitoring plan. Document fields include material batch numbers, weighing and feeding values, process execution parameters, test results, deviation descriptions, and more. Operations must strictly follow GMP rules that require documents to align with on-site actions.
Constraints imposed on multi-turn dialogue and prompt engineering
Data for this category is updated per batch and critical process step. Multi-turn dialogue must retain contextual links for each batch to avoid mixing data across batches. Document fields contain precise numerical and batch information. Prompts must explicitly require referencing corresponding material batch numbers and process parameters, rather than using vague descriptions. GMP rules require documents to align with on-site operations. Process documents generated via dialogue must match the current batch’s execution records, and cannot generate content without relying on actual production data. When summarizing deviation and test data, links to corresponding equipment and environmental monitoring data must be included. Multi-turn dialogue must gradually guide users to supplement missing associated fields, to avoid generating incomplete exception reports.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Context window retention rule | Retain the first 15 conversations and associated batch record data | Match the full process context of each batch to avoid cross-batch interference |
| Variable binding configuration | Bind material batch numbers and process parameter variables for the current batch | Ensure generated content references exclusive data for the current production batch |
messages format specification | Use a structured format of "system prompt + user question + tool response" | Standardize the structure of exception reports and process documents output by the model, comply with GMP document requirements, and align with FastGPT v4.17.0 dialogue format rules |
variables real-time call | Call fields from the current batch’s electronic batch records in real time | Ensure generated content stays synchronized with actual production data, avoiding outdated static data |
| Maximum context length | 8000–12000 characters | Accommodate complete process and test data for a single batch, preventing content truncation |
chatId isolation rule | Isolate chatId by production batch | Prevent production data from different batches from mixing, ensuring context accurately corresponds to a single production batch |
Three Common Configuration Errors
- Issue: Model-generated reasoning content is not correctly identified, and the interface displays blank content or has formatting errors. Cause: Failed to replace the
<think></think>tags according to FastGPT format requirements, or failed to correctly configure themessagesrole field to distinguish between reasoning and output content. - Issue: After switching AI models, previously configured prompts fully fail, and generated content does not meet GMP document requirements. Cause: Failed to configure dedicated system prompts for different models, or failed to bind compatible parameters for the corresponding model in
variables. - Issue: The dialogue application cannot connect to the integrated quality management system plugin, and cannot automatically pull batch record data. Cause: Failed to correctly configure the session binding rules between
chatIdand the plugin, causing the plugin to fail to obtain context information for the current production batch.
How to Verify Successful Configuration
- Generate a draft batch production record for a single batch, and check whether the generated content includes the currently bound material batch number and process parameters.
- Switch between different AI models, verify that the prompt guidance effect remains consistent, and ensure that the configured
variablescan be called normally across different models. - Review the dialogue log, confirm that the structured format of
messagesmeets requirements, with no missing role fields or formatting errors. - Trigger the plugin connection action, verify that the current batch’s electronic batch record data can be automatically pulled, and that the generated exception report includes complete deviation and test association information.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.