Multi-Turn Conversations and Prompts for Deviation and CAPA

Data Characteristics

Deviation and CAPA (Corrective and Preventive Action) product data originates from pharmaceutical quality management systems (QMS). This includes deviation reports, investigation records, root cause analyses, CAPA plans, implementation records, and effectiveness verification reports. Data exists as a mix of structured (e.g., database records) and unstructured formats (e.g., Word documents, PDF reports, image attachments). Update frequency depends on the real-time nature of events and their processing; new deviation or CAPA records generate continuously, and older records update through subsequent verification. Document structures typically include standard fields like deviation number, occurrence date, impact assessment, responsible person, estimated completion date, and actual completion date, alongside extensive free-text descriptions.

Constraints from Data Characteristics on Multi-Turn Conversations and Prompts

The high timeliness and complexity of deviation and CAPA data require multi-turn conversational systems to quickly retrieve and understand recently updated records. Extensive free-text descriptions, especially in root cause analysis and effectiveness verification sections, demand high semantic understanding from the model to extract key information from unstructured text. Historical conversation context is crucial for understanding user intent and tracking deviation processing progress. For example, a user might mention a deviation number in one turn and then inquire about its CAPA plan in a subsequent turn. Accurate identification and referencing of specific fields (e.g., deviation number, responsible person, completion date) within the conversation are key to ensuring information accuracy.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`6` turns	Balances historical information relevance with model processing efficiency, reducing unnecessary long contexts.
`Chunk size` (Chunk Size)	`500` characters	Accommodates paragraph lengths in deviation and CAPA reports, ensuring semantic completeness.
`Recall count` (Recall Count)	`8` items	Covers multiple highly relevant deviation or CAPA records, increasing information coverage.
`Similarity threshold` (Similarity Threshold)	Calibrate by actual measurement	Adjusts based on the specific dataset's semantic distribution to balance recall and precision.
`Rerank result count` (Rerank Return Count)	`4` items	Focuses on the few most relevant pieces of information, reducing user reading burden.
`Conversation Log Retention Days` (Conversation Log Retention Days)	`90` days	Meets common requirements for historical event traceability in production quality management.

Common Pitfalls

Symptom: When a user asks about the progress of a specific deviation, the system replies with unrelated information about other deviations. Reason: Recall count (Recall Count) is too low or Similarity threshold (Similarity Threshold) is too high, leading to a failure to recall the correct or sufficient relevant documents.
Symptom: Users experience long reply times, sometimes exceeding 20 seconds. Reason: Chunk size (Chunk Size) is set too large, resulting in excessive text volume for single retrievals and model processing, or maxContext is too long, increasing the computational load for the model to understand context.
Symptom: The system cannot accurately identify and extract key fields like root cause or preventive measures from deviation reports. Reason: Instructions for extracting these specific fields in the prompt are not clear enough, or training data does not sufficiently cover this type of information.

Verification of Configuration

Conduct multi-turn conversation tests to ensure the system correctly maintains context and provides relevant responses based on previous turns.
Randomly select 10 representative deviation or CAPA queries. Check if the documents recalled by the system contain correct and complete key information, such as deviation number, status, and responsible person.
Test the system's ability to understand deviation reports with different document structures (e.g., Word, PDF), focusing on extracting key information like root cause and impact assessment from free text.
Monitor system response times to ensure reply times remain within an acceptable range under expected user loads and query complexity.

The values provided are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.