Multi-Turn Conversations and Prompts for Clinical Trial

Data Characteristics in This Category

Data for clinical trial pre-screening on pharmaceutical e-commerce platforms primarily originates from drug inserts, clinical research reports, de-identified patient medical histories, medication records, and trial protocols. This data updates frequently, especially drug inserts and clinical trial progress, often quarterly or annually. Document structures vary: drug inserts are typically structured or semi-structured text, containing standard fields like indications, contraindications, dosage, and adverse reactions. Clinical research reports are mostly unstructured long texts, covering research background, methods, results, and discussion. Patient data may exist as structured database records, including disease codes, diagnosis dates, and treatment plans. Fields and units are highly specialized, for example, dosage units (mg, g, ml), frequency (once daily, twice weekly), disease codes (ICD-10), and laboratory indicators (mmol/L, U/L).

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

The highly specialized nature and frequent updates of pharmaceutical e-commerce platform data demand strict accuracy and timeliness from multi-turn conversations and prompts. Specialized terminology and dosage units in drug inserts require prompts to precisely understand and generate medically compliant responses. The unstructured nature of clinical research reports makes extracting key inclusion/exclusion criteria from long texts challenging, necessitating more complex prompt engineering to guide the model's focus. De-identification and privacy protection of patient data constrain how the model handles personal information in multi-turn conversations; prompts must clearly limit the scope of information output. Rapid data updates require the knowledge base to synchronize the latest information promptly and ensure prompts recall the most current drug information or trial protocols, preventing inaccurate pre-screening results due to outdated information.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale for This Value
`max_tokens`	`512`	Ensures a moderate response length, avoiding redundancy while covering common pre-screening questions.
`temperature`	`0.3`	Reduces the randomness of model-generated responses, enhancing certainty and accuracy of medical information output.
`top_p`	`0.8`	Prioritizes high-probability words while ensuring diversity, preventing the generation of irrelevant medical terminology.
`Recall Count`	`Top 5`	Balances recall efficiency and relevance, ensuring a sufficient but not excessive number of potentially matching knowledge snippets.
`Similarity Threshold`	`0.005`	Improves the precision of recall results by leveraging the high discriminative power of medical terminology, filtering out low-relevance content.
`System Prompt`	See below	Guides the model to act as a professional pharmaceutical e-commerce pre-screening consultant, strictly adhering to medical guidelines.

Three Common Pitfalls

Inconsistencies between the context output by the AI chat node and the AI's response occur because the System Prompt fails to clearly distinguish between conversation history and the logic for generating the final response.
When embedding the chat page in a mini-program, the top title is modified. This is due to conflicts between the default styles or scripts of the front-end component library and FastGPT's embedding code.
The system prompt of the AI node in the workflow does not strictly limit product information fields, causing the model to generate content beyond the predefined six categories. This happens because the restrictive statements in the prompt are not strong enough or use vague wording.

How to Verify Configuration

Construct typical patient consultation scenarios to check if the model accurately identifies drug names, dosage units, and disease codes in multi-turn conversations.
Verify if the model can immediately reference and respond with information from the latest drug inserts or clinical trial protocols after a knowledge base update, checking the effectiveness of the Refresh Interval parameter.
Evaluate if the model can accurately cite and explain content from relevant clinical research reports when faced with patient questions about inclusion/exclusion criteria, assessing the reasonableness of Recall Count and Similarity Threshold.

The values provided are common starting points and should be measured 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.