Multi-Turn Conversations and Prompts for Structured Analysis

Data Characteristics

Stability study data comes from various sources: experiment reports, batch production records, and analytical method validation reports. These documents are typically PDFs, Word files, or Excel spreadsheets. Update frequency varies based on the product development phase and regulatory requirements, ranging from monthly to annually. Document structures are highly standardized, adhering to ICH guidelines and pharmacopoeia requirements. They include clear section headings such as "Research Objective," "Sample Information," "Test Items," "Results Analysis," and "Conclusion." Fields include temperature, humidity, time, batch number, test indicators (e.g., content, dissolution, impurities), units (e.g., °C, %RH, h, mg/mL, %), and statistical parameters (e.g., RSD, confidence interval). Some data may be in scanned documents, requiring OCR.

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

The standardized structure and clear field units in stability study documents provide an advantage for semantic understanding in multi-turn conversations based on structured analysis. However, specialized terminology and acronyms (e.g., "ICH Q1A," "RSD") require prompts to accurately recognize domain-specific vocabulary. Multi-turn conversations must handle complex conditional queries. For example, "Query the content change for batch number 20230101 at 6 months under 30°C/65%RH conditions." This requires the system to accurately extract multi-variable information and establish logical connections. For tabular data, especially cross-page or complex nested tables, parsing accuracy directly impacts the reliability of conversation results. The cyclical nature of data updates means the knowledge base requires regular maintenance and incremental updates to ensure conversations rely on the latest data.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192` tokens	Stability study documents often have strong contextual relevance, requiring a longer context window to understand multi-turn conversation semantics and traceability.
`Chunk size` (Segment Length)	`500–700` characters	Balances semantic completeness and segment recall efficiency. Avoids long paragraphs diluting key information or short paragraphs breaking context.
`Recall count` (Recall Count)	`8–12` items	Ensures coverage of multiple relevant sections or tabular data in stability study reports, improving answer accuracy.
`Similarity threshold` (Similarity Threshold)	`0.75`	Improves recall precision for specialized terminology and standardized descriptions, reducing interference from irrelevant information.
`Rerank result count` (Reranked Return Count)	`5` items	Reranks initial recall results to further prioritize document snippets most relevant to the user's query intent.
`PARSE_FILE_TIMEOUT_SECONDS`	`600` seconds	Stability study documents may contain numerous charts and complex tables, making parsing time-consuming. A longer timeout is necessary.

Three Common Pitfalls

Incomplete or garbled data in conversation responses. This may be due to an improperly set chunk size for backend API streaming output, leading to incomplete data packet segmentation or encoding issues during frontend reception.
When a user queries a specific test indicator for a particular batch, the result is empty or inaccurate. This typically occurs when document parsing fails to correctly identify the batch_id field or the assay_value field in tables, or when OCR recognition of scanned documents has errors.
The system cannot correctly recognize specialized acronyms like "RSD" or "ICH Q1A," preventing matching with relevant knowledge points. This is because the prompts lack pre-set recognition rules or a synonym dictionary for these industry-specific terms.

How to Verify Configuration

Select a stability study report with complex tables and multiple pages. Ask multi-turn questions and verify that the key fields assay_value and batch_id returned by the system match the original text.
For queries involving different time points or storage conditions (e.g., 25°C/60%RH), check if the returned results accurately differentiate and associate with the correct experimental data.
Use queries containing industry acronyms (e.g., "RSD," "ICH Q1B") to verify if the system can correctly understand and recall document snippets containing these terms, assessing their embedding quality in the knowledge base.

The values provided are common starting points. Measure performance 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.