Data Characteristics
Process validation data originates from production batch records, quality control reports, equipment calibration records, and standard operating procedures. These documents are typically in PDF, Word, or structured database formats like LIMS. Data updates are infrequent, usually occurring after each production batch or at the end of an equipment calibration cycle. Document structures are rigorous, containing extensive technical terminology, charts, and data tables. Key fields include batch number, production date, Critical Quality Attribute (CQA) metrics, Critical Process Parameter (CPP) ranges, deviation records, and Out-of-Specification (OOS) reports. Units involve mass (mg, g, kg), volume (mL, L), temperature (°C), pressure (kPa), and concentration (% w/v, ppm).
Constraints on Multi-Turn Conversations and Prompts
The rigorous and specialized nature of process validation data requires the dialogue system to accurately understand technical terms and process information from tables and charts. Infrequent updates mean historical data queries are more critical. The dialogue system needs robust retrieval capabilities to access detailed records for specific batches. Complex document structures, including significant non-textual information, necessitate effective information extraction and structuring during data preprocessing. In multi-turn conversations, users may progressively refine query conditions, for example, from "dissolution data for a specific batch" to "reasons for abnormal dissolution in that batch." This requires the system to maintain context and dynamically adjust retrieval strategies and prompt generation based on follow-up questions. Precise understanding of units is also crucial to avoid misinterpretations due to unit confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Balances context completeness with retrieval efficiency, preventing information loss from excessive fragmentation. |
maxContext | 8–12 turns | Covers typical multi-turn follow-up scenarios, maintains conversational coherence, and prevents forgetting early key information. |
recallTopK | 10–15 items | Improves initial recall rate, ensures potential relevant documents are included, and provides sufficient candidates for subsequent re-ranking. |
rerankTopN | 3–5 items | Refines final results, highlights the most relevant items, and reduces model processing complexity. |
similarityThreshold | 0.75–0.85 | Balances recall precision and recall rate, filters out low-relevance documents, and reduces noise. |
promptTemplate | Include fields like batch number, CQA, CPP | Guides the model to focus on core process validation information, improving the professionalism and accuracy of responses. |
Common Mistakes
- Issue: The system fails to correctly identify specific batch numbers mentioned by the user, leading to empty or inaccurate retrieval results. Reason: Batch numbers exist in various formats or locations within documents, but were not uniformly extracted or standardized during preprocessing.
- Issue: When a user asks about the unit of a certain metric, the system provides an incorrect or default unit that does not match the actual data. Reason: Unit information from the data source was not effectively extracted and associated with the corresponding values, or the prompt did not emphasize the importance of units.
- Issue: After the third turn of conversation, the system "forgets" details from earlier questions, and answers begin to deviate from the topic. Reason: The
maxContextparameter is set too low, causing historical conversation context to be truncated, preventing the model from accessing the complete dialogue history.
Configuration Validation
- Select multiple representative process validation documents. Simulate multi-turn questioning. Verify the system's ability to accurately understand and cite key information such as batch numbers, CQAs, and CPPs from the documents.
- For numerical values with different units in the documents, ask unit-related follow-up questions. Ensure the system can correctly identify and convert or specify the original units.
- Design test cases with 3-5 complex follow-up questions. Observe if the system maintains contextual coherence in later stages of the conversation and provides accurate answers based on details from earlier questions. This evaluates the effectiveness of
maxContext.
The values provided are common starting points. Measure against specific 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.