Multi-turn Conversations and Prompts for Structured Analysis

Data Characteristics

Deviation and Corrective and Preventive Action (CAPA) documents are critical in biopharmaceutical R&D. These documents typically originate from Quality Management Systems (QMS), Manufacturing Execution Systems (MES), or Laboratory Information Management Systems (LIMS). They are stored in formats like PDF, Word, or structured text. Document content includes event descriptions, root cause analysis, impact assessments, corrective actions, preventive actions, and effectiveness verification. Update frequency aligns with deviation events and CAPA execution progress, which can be daily, weekly, or monthly. Core fields include event number, occurrence time, involved product batches, deviation level, root cause code, responsible person, planned completion date, and actual completion date. Units commonly appear for time periods (e.g., hours, days), quantities (e.g., pieces, batches), and percentages (e.g., pass rate).

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

The semi-structured nature of Deviation and CAPA documents requires the AI model to have deep semantic understanding for extracting key information from free-text descriptions during multi-turn conversations. Documents contain extensive specialized terminology and acronyms, demanding high coverage of biopharmaceutical domain knowledge to avoid semantic misunderstandings. Furthermore, the chained dependencies in CAPA processes (e.g., a CAPA might be triggered by multiple deviations, or its effectiveness verification depends on subsequent batch data) necessitate support for complex logical reasoning and context tracking in multi-turn conversations. For expired or invalid CAPA records, prompt design must clearly indicate their status to prevent referencing outdated information. Consistency in units for time, batch, and other field values also requires the model to perform unit conversion or validation when generating responses.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8` turns	Maintains conversational coherence, covering common traceability depth for deviation analysis and CAPA correlation.
`Chunk size` (Segment Length)	`500` characters	Balances semantic completeness with retrieval efficiency, adapting to the paragraph structure of deviation reports.
`Recall count` (Retrieval Count)	`10` items	Ensures coverage of multiple relevant sections, such as deviation description, root cause, actions, and verification.
`Similarity threshold` (Similarity Threshold)	`0.75`	Filters out low-relevance document snippets, improving answer accuracy.
`Rerank result count` (Reranked Return Count)	`3` items	Prioritizes the most relevant core information, reducing user reading burden.
`promptTemplate`	Includes "Summarize CAPA status and effectiveness based on deviation number, involved product, and root cause."	Guides the model to focus on core elements of deviation and CAPA, avoiding vague responses.

Three Common Mistakes

Calling the chat interface returns an unAuthChat error. This is typically due to an incorrect or expired API_KEY configuration.
AI chat results contain non-JSON formatted newlines, causing downstream systems to fail parsing. This can happen if the model does not strictly adhere to JSON format or escape special characters when generating JSON.
Uploading large PDF or Word files results in a "file processing timeout" message. This occurs when the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, insufficient for parsing complex documents.

How to Confirm Correct Configuration

For a typical deviation number, use multi-turn conversations to trace associated CAPA records. Verify the accuracy of key fields (e.g., root cause, actions, completion date).
Input an incomplete deviation description and observe if the system guides the user to provide more necessary information, verifying the guiding effect of the promptTemplate.
Upload a CAPA document containing complex tables and specialized terminology. Verify if the model can correctly extract table data and understand specialized terms by comparing the extracted results with the original text to determine retrieval and parsing effectiveness.

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.