Data Profile for This Category
Data for pharmaceutical dosage form manufacturing originates from approved formulation processes, material batches, MES/electronic batch records, equipment and environmental monitoring systems, LIMS, and quality management systems. Data is updated per production batch, each critical process step, and according to fixed monitoring plans.
Daily documents include master production files, batch production records, batch packaging records, cleaning records, inspection records, deviation/CAPA/change documents, and batch release review forms. Fields cover material batch numbers, process parameter values, test results, deviation descriptions, and other relevant fields. Corresponding units include degrees Celsius, hours, batch numbers, and others.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Data for this category is updated per batch and has a complex structure. This requires multi-turn dialogue to isolate context by production batch to avoid mixing cross-batch data.
Documents include multiple types of compliance records, so prompts must explicitly limit retrieval to the current batch’s batch production, inspection, and other document scopes. Fields have specific units, so prompts must require the AI to accurately identify and associate numerical values with their corresponding units.
Additionally, GMP compliance requires data traceability. The messages for multi-turn dialogue must retain the full interaction chain, and variables can pass the current batch number as a retrieval filter condition to ensure the dialogue only links to data from the current production batch.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bound to the current production batch number | Isolate conversation context for the same batch, prevent cross-batch data confusion, and meet GMP data traceability requirements |
messages | Retain the last 3 rounds of interaction context, with single-round content not exceeding 1200 characters | Balance context completeness and model processing efficiency, adapt to the interaction logic of multi-turn audits |
variables | Pass the current batch number and bill of materials ID | Filter knowledge base retrieval scope, only call compliance documents such as batch production and inspection records for the current batch |
| Timeout | 600 seconds | Adapt to the average time required for GMP audits, avoid workflow interruptions due to long wait times |
| Single Search Limit | Top 5 results | Adapt to the number of compliance documents for this category, avoid retrieving excessive irrelevant content |
| Maximum Context | 8000–10000 token, calibrated via actual testing | Adapt to the total length of multiple types of compliance documents, avoid exceeding the model’s processing limit |
| Question Optimization | Enabled, with optimization direction limited to compliance audit-related content | Focus on core requirements of supplier and process quality audits, reduce interference from irrelevant content |
Three Common Misconfigurations
- Phenomenon: The conversation node directly outputs the original AI-generated content without processing via a text splicing component. Cause: The output interception rule for the conversation node in the workflow is not configured, causing AI-generated content to be exposed directly on the front end, which does not meet the process requirements of compliance audits.
- Phenomenon: After knowledge base retrieval results are passed to AI dialogue, the document source and corresponding fields cannot be correctly associated. Cause: Retrieval results are not organized into a structured format with document type and field values, making it impossible to match the field structure of pharmaceutical dosage form manufacturing documents.
- Phenomenon: In multi-turn dialogue, the output from the previous round of AI is not correctly passed to the next round of dialogue. Cause: The context transfer node in the workflow is not configured, and the
messagescontent from the previous round is not synchronized to subsequent conversation nodes, causing the audit logic to interrupt.
How to Confirm Proper Configuration
- Check the
chatIdconfiguration, confirm it is bound to the current production batch number. Use the test function in FastGPT v4.17.0 to initiate a conversation, and verify that the context only includes relevant data for that batch. - Verify the
messagescontext retention rule: initiate more than 3 rounds of dialogue, confirm that only the specified number of interaction rounds are retained, and that single-round content does not exceed the set length limit. - Test the knowledge base retrieval format, check whether the content passed to the AI includes document type and field values, and confirm that it meets the document structure requirements for pharmaceutical dosage form manufacturing.
- Verify multi-turn context transfer: initiate two consecutive conversations, confirm that the second conversation can obtain the output content from the first conversation as input.
The values provided here are common starting points and should be evaluated against the reader's own samples.