Multi-turn Conversations and Prompts for Deviation and CAPA

Data Characteristics

Deviation and Corrective and Preventive Action (CAPA) regulatory submission data primarily originates from an organization's quality management system records. This includes deviation reports, investigation reports, root cause analyses, CAPA plans, implementation records, and effectiveness verification reports. Data typically exists as structured documents (e.g., Word, PDF reports) and unstructured text (e.g., interview transcripts, meeting minutes). Data updates frequently, generated and archived in real-time as deviation events occur and the CAPA process progresses. Document structures are relatively fixed, containing key fields such as event description, impact assessment, interim measures, root cause, corrective actions, preventive actions, responsible parties, and completion deadlines. Some fields may include specific technical terms, abbreviations, and units of measurement (e.g., ppm, ℃, mg/mL).

Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts

The real-time and structured nature of deviation and CAPA data requires multi-turn dialogue systems to quickly index and understand the latest document content, ensuring the accuracy of submission materials. The specialized terminology and abbreviations in these documents necessitate prompt design that considers precise domain vocabulary matching and contextual understanding to avoid misunderstandings due to lexical ambiguity. The multiple key fields and their interrelationships within documents constrain the dialogue system to guide users through multi-turn questioning to complete information, for example, by inquiring about the logical relationship between "root cause" and "corrective actions." Furthermore, the rigor of the CAPA process requires the system to verify the completeness and consistency of data across different stages when generating or modifying materials. This includes ensuring that all identified deviations have corresponding CAPA plans and that measures within the plans have documented effective execution.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`4096`	Ensures the system can accommodate the full content of a single deviation report or CAPA plan, aiding contextual understanding.
`Chunk size`	`800–1000 characters`	Balances text block completeness with model processing efficiency, preventing truncation of critical information.
`Recall count`	`8–12 entries`	Improves the accuracy of recalling relevant deviation or CAPA records, covering potentially related information.
`Similarity threshold`	`0.75–0.85`	Filters out irrelevant document segments, ensuring the precision of recalled content, especially for specialized terminology.
`Rerank result count`	`5 entries`	Prioritizes displaying a few key pieces of information most relevant to the current conversation, improving user efficiency in obtaining valid information.
`maxToken`	`1500`	Provides sufficient generation space to handle detailed deviation analyses or CAPA measure descriptions.

Common Pitfalls

During a multi-turn conversation, the system failed to correctly identify a user's follow-up question regarding a specific deviation number, leading to the return of irrelevant CAPA records. The prompt did not explicitly guide the model to identify and extract the deviation number as a key entity, causing a deviation in contextual understanding.
When a user inquired about the execution status of a CAPA measure, the system directly responded "not found," even though relevant records existed in the database. The knowledge base indexing strategy did not sufficiently consider synonymous expressions or abbreviations for the execution status field, resulting in recall failure.
An HTTP request in the workflow returned a 404 error, preventing the retrieval of the latest regulatory documents and affecting the accuracy of deviation assessments. The HTTP request configuration's URL or authentication parameters were expired or incorrectly configured.

How to Verify Configuration

Simulate user questions to verify the system's ability to accurately identify and extract key fields such as deviation number, occurrence date, and root cause, and retrieve relevant documents based on these fields.
Test multi-turn dialogue scenarios to check if the system can remember entities and intentions mentioned in previous turns and conduct coherent follow-up questions or information supplements based on them.
Select deviation reports and CAPA plans of varying complexity to verify if the system correctly cites original document content and maintains the accuracy of specialized terminology when generating draft submission materials.

Note: The values provided are common starting points. Measure them against your 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.