Multiturn Conversation and Prompts for Peptide Drug

Data Characteristics

Peptide drug pharmacovigilance data comes from clinical trial reports, real-world study (RWS) data, post-marketing surveillance reports, and global regulatory adverse event databases (e.g., FDA FAERS, EMA EudraVigilance). This data updates frequently, typically weekly or monthly, to capture the latest adverse reaction signals. Documents are often structured or semi-structured reports. They include patient demographics, medication history, adverse reaction descriptions, severity, outcomes, and relevant laboratory indicators. Adverse reaction descriptions are usually free text, mixing medical terminology and natural language. Laboratory indicators like liver and kidney function, or electrolyte levels, require strict adherence to medical standard units, such as mg/dL or mmol/L.

Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts

The complex data sources and high update frequency for peptide drugs require the multiturn conversation system to integrate the latest data in real-time or near real-time. This ensures accuracy and timeliness of conversation content. Semi-structured and free-text report formats mean prompt design must effectively extract key information and identify synonyms and abbreviations for medical terms. The detailed and specialized nature of adverse reaction descriptions challenges the semantic understanding capabilities of multiturn conversations. The model needs to differentiate adverse reaction severity and causality. Furthermore, peptide drug specificities, such as immunogenicity and drug interactions, require targeted knowledge retrieval and reasoning during conversations to avoid information omission or misjudgment. The precise unit requirements for laboratory indicators also necessitate unit standardization and validation when prompts process numerical information.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`3000–4000 characters`	Covers key medical descriptions and patient background information in peptide drug adverse reaction reports, ensuring context completeness.
`Recall Count`	`Top 8`	Considering the diversity of potential adverse event signals for peptide drugs, increasing the recall count helps cover a broader range of knowledge points.
`Similarity Threshold`	`0.75`	Ensures recalled knowledge snippets are highly relevant to the query, filtering out distracting information with similar medical terms but different meanings.
`Segment Length`	`400–600 characters`	Balances information density of individual knowledge snippets with model processing efficiency, adapting to long sentences and detailed descriptions common in medical reports.
`Rerank Return Count`	`Top 3`	From a high recall count, reranking selects the most relevant few snippets, improving the precision of multiturn conversations.
`Timeout`	`60 seconds`	Handles complex queries that may involve multiple knowledge base retrievals and reasoning, ensuring sufficient time for response and avoiding `HTTP 504` errors.

Three Common Mistakes

The conversation log contains many duplicate or irrelevant knowledge points. This happens when the Similarity Threshold is set too low, recalling too much low-quality information.
When processing patient medication history, the AI Conversation Module fails to correctly identify various spellings of drug names. This occurs because the prompt does not include enough synonyms or alias mapping rules.
During the conversation, the AI Conversation Module cannot process image-formatted data uploaded by the user, such as medical imaging report screenshots. This is because the workflow lacks a parser for jpg or png file types.

How to Confirm Correct Configuration

Use FastGPT's Conversation Log feature to check if the model accurately identifies and extracts key medical terms and numerical values for peptide drug adverse reactions during multiturn conversations. Evaluate if key fields such as Adverse Reaction Description and Severity are parsed correctly.
Simulate various typical peptide drug adverse reaction report scenarios, including free-text descriptions and structured data. Verify the model's responses to different data sources, ensuring the conversation correctly guides to relevant knowledge bases.
Randomly sample a percentage of conversation records. Manually compare the model's adverse reaction judgments with expert judgments, especially its ability to identify rare adverse events. Adjust the Similarity Threshold based on actual business needs.

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