Multi-turn Conversations and Prompts for Stability Study

Data Characteristics in this Category

Stability study data primarily comes from drug development batch production records, quality control reports, and long-term stability observation reports. This data typically exists as structured tables (e.g., CSV, Excel), unstructured text (e.g., experimental records, analysis report PDFs), and database records. Data updates continuously throughout the drug lifecycle. New batch productions, new stability observation points, and changes in analytical methods all trigger updates. Document structures vary, but core data usually includes batch number, production date, expiration date, storage conditions, test items, test methods, test results (values and units), and judgment criteria. Key fields include batch_number, sample_ID, time_point, temperature, humidity, content, impurity_A, solubility, pH_value. Units involve percentages, ppm, mg/mL, Celsius, and others.

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

The highly structured and numerical nature of stability study data requires the multi-turn conversation system to accurately identify numerical ranges, units, and time series when understanding user intent. For example, a user might ask, "Is the impurity_A content for batch ABC-202301 at 12 months under 30°C/65%RH conditions out of specification?" Here, the prompt needs to guide the model to focus on these key parameters. Descriptions of experimental procedures in unstructured reports require the model to extract key information from long texts to support more complex queries, such as "Analyze the trend of solution_color change in the batch_XYZ stability report." The continuous data update characteristic means the system needs to regularly synchronize the knowledge base and ensure the model perceives data freshness, avoiding judgments based on outdated information. Unit sensitivity is another constraint; prompt design must emphasize unit identification and conversion to prevent misjudgments due to unit confusion.

Configuration Settings

Configuration Item	Recommended Value	Rationale for this Value
`maxContext`	`8`	Stability study queries often involve multiple parameters and historical data points. 8 turns are sufficient to cover most complex scenarios.
`Chunk size`	`800–1200 characters`	Experimental descriptions and result tables in stability reports are information-dense. A longer segment length helps maintain contextual completeness.
`Recall count`	`Top 5 entries`	Ensures that enough relevant batch data and testing standards are recalled in multi-turn conversations to provide a basis for judgment.
`Similarity threshold`	`0.75`	Stability data queries typically require high-precision matching, avoiding the recall of irrelevant batches or test items.
`Rerank result count`	`Top 3 entries`	After reranking, the top 3 entries usually contain the most core and relevant batch stability data or judgment criteria.
`System Prompt`	Calibrate by measurement	Must include identification and emphasis on `batch_number`, `time_point`, `storage_conditions`, `test_item`, and `judgment_criteria`.

Three Common Mistakes

The model omits key values or units in its answers because the prompt does not explicitly require the model to output all relevant values and units.
After multiple follow-up questions, the model fails to remember a specific batch_number mentioned in previous turns because maxContext is set too low, causing early conversation history to be discarded.
The system returns "No relevant information found" even when data exists in the knowledge base. This might be because Similarity threshold (similarity threshold) is set too high, leading to overly strict recall that cannot match subtle variations in user query phrasing.

How to Confirm Proper Configuration

Conduct multi-turn questioning for different batches, test items, and storage conditions. Check if the model can accurately identify and reference key fields like batch_number, time_point, temperature, and humidity.
Test queries involving numerical ranges and units, for example, "Which batches have impurity_B content exceeding 0.1%?". Verify the accuracy of values and units in the model's response.
Simulate a user gradually refining query conditions in a conversation. For instance, first ask for "stability data for batch_X," then ask for "content data at 6 months." Confirm that the model can respond correctly based on the context of previous conversations.

The values given 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.