Data Characteristics for This Category
Deviation and Corrective Action and Preventive Action (CAPA) data originates primarily from enterprise Quality Management Systems (QMS), Manufacturing Execution Systems (MES), and Laboratory Information Management Systems (LIMS). This data typically exists in structured or semi-structured formats, such as incident reports, investigation records, root cause analysis reports, corrective and preventive action plans, and verification reports. Update frequency depends on the real-time nature of incident occurrence and processing, usually daily or real-time. The document structure is based on standard quality management processes, including fields like incident description, occurrence time, involved product batch, responsible person, root cause, corrective actions, preventive actions, implementation plan, and verification results. The product batch field may contain alphanumeric batch numbers, and time fields require precision to the minute.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The real-time and structured nature of Deviation and CAPA data requires workflows to respond quickly to new event entries and accurately extract key information. The multi-system origin means workflows need to support various API interfaces or data synchronization mechanisms for data ingestion. Common free-text descriptions in reports demand strong information extraction capabilities from Large Language Models (LLMs), requiring prompt engineering to guide them in identifying core elements. Additionally, due to the strictness of Deviation and CAPA processes, workflows must include rigorous verification and audit steps to ensure the compliance and effectiveness of actions. Precise identification of sensitive fields like product batch and time also requires workflows to have high accuracy in data parsing to avoid information discrepancies caused by format errors.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 4096 | Accommodates longer free-text descriptions in deviation reports, ensuring information completeness. |
Chunk size (Segment Length) | 500 characters (characters) | Balances semantic integrity of text with recall efficiency, avoiding excessive segmentation. |
Recall count (Number of Retrieved Items) | Top 8 entries (top 8) | Covers key event descriptions, root causes, and actions, providing sufficient context. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures retrieved content is highly relevant to the current query, reducing interference from irrelevant information. |
Rerank result count (Number of Reranked Items) | Top 3 entries (top 3) | Focuses on the most relevant root causes or actions, improving the precision of the answer. |
PARSE_FILE_TIMEOUT_SECONDS | 180 seconds (seconds) | Allows processing of large investigation reports or attachments, preventing parsing timeouts. |
Three Common Pitfalls
- Symptom: Key fields (e.g., batch number, responsible person) in the workflow's returned results are empty or incorrectly formatted. Reason: Data preprocessing lacks sufficient compatibility with different source data formats, or prompts do not explicitly specify information extraction rules.
- Symptom: After calling the workflow API, the AI's thought process or intermediate steps are unavailable. Reason: The option to return detailed thought processes was not enabled or correctly configured in the
request workflowAPI parameters. - Symptom: The workflow frequently times out when processing specific reports, leading to task failure. Reason: Parameters like
PARSE_FILE_TIMEOUT_SECONDSare set too low, failing to accommodate the parsing time for some large or complex documents.
How to Confirm Proper Configuration
- Initiate a session via API and observe the accuracy of key structured information like batch numbers and occurrence times in the returned results. Compare these with the original data.
- Check workflow execution logs to confirm that all nodes (e.g., data extraction, LLM calls, external API integration) executed successfully and without obvious error codes.
- Simulate user queries for typical Deviation and CAPA cases. Verify that AI responses accurately cite relevant report content and provide reasonable action recommendations.
- Adjust the
Similarity threshold(Similarity Threshold) parameter and test the relevance of retrieved results for different queries. This ensures the threshold effectively filters information.
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.