Product Usage: Multi-Turn Conversations and Prompts for

Data Characteristics

Biopharmaceutical product usage data typically originates from product inserts, user manuals, FAQs, technical support documents, and clinical application guidelines. Update frequency for these documents depends on product iterations and regulatory requirements, potentially quarterly or annually. Documents are highly structured, often containing detailed product ingredients, mechanisms of action, indications, dosage and administration, contraindications, adverse reactions, and storage conditions. Fields and units are highly specialized, for example, dosage units (mg/kg), concentration (% w/v), administration routes (intravenous injection, oral), and specific medical terminology and coding. Some data may exist in chart format, describing pharmacokinetic or pharmacodynamic parameters.

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

The highly specialized and structured nature of product usage documentation requires multi-turn dialogue systems to accurately identify medical terminology and dosage units when understanding user queries, avoiding semantic deviation. Low update frequency but rigorous content means knowledge base construction must prioritize content accuracy and authority, establishing strict version control mechanisms. Chart information within documents presents a challenge for AI models to directly parse and integrate into multi-turn conversations, requiring additional chart recognition or data extraction capabilities. Furthermore, when dealing with sensitive information such as contraindications and adverse reactions, the dialogue system must phrase responses carefully to ensure compliance and avoid misleading users. This is particularly crucial in prompt design, which needs to emphasize safety and risk warnings.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`2000 characters`	Ensures coverage of longer user descriptions and product details in multi-turn conversations while controlling model inference costs.
`Recall Count`	`Top 5`	Product usage questions typically have clear answers; a small number of high-quality recalls effectively improves accuracy and avoids interference from irrelevant information.
`Similarity Threshold`	`0.85`	Terminology in the biopharmaceutical field is precise; a high threshold helps filter out document segments with low semantic similarity but different actual meanings.
`Rerank Return Count`	`3`	Further refines the most relevant segments based on high-similarity recall, improving response precision.
`Segment Length`	`500 characters`	Balances information completeness and model processing efficiency, preventing excessively long paragraphs from diluting key information or overly short paragraphs from losing context.
`promptTemplate`	Calibrated by actual testing	Prompts must include fixed requirements such as user safety warnings and disclaimers, and guide the model to extract key information, ensuring response rigor.

Three Common Mistakes

"404" errors in conversations usually result from incorrect backend API address configuration or an inactive service.
History query fails to filter by customUid, returning all session records. This indicates the history retrieval logic does not correctly parse or apply the customUid parameter.
A code execution node after an AI dialogue node still displays "thinking content." This may be because the output of the AI dialogue node in the workflow does not fully cover or replace the "thinking content" placeholder.

How to Confirm Correct Configuration

Conduct multi-turn question-and-answer tests for specific product usage questions. Verify whether the answers accurately cite key information from product inserts, such as dosage 10 mg/kg or administration route intravenous injection.
Simulate user questions involving contraindications or adverse reactions. Check whether model responses include necessary risk warnings and disclaimers, and avoid providing diagnostic advice.
Initiate conversations under different customUid values. Verify the history query function, ensuring each customUid can only retrieve its corresponding session records.
Ask questions about product principles or mechanisms of action. Observe whether the model can explain coherently, without obvious logical errors or information fragmentation. This reflects the effectiveness of context management and maxContext configuration.

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.