Multi-turn Conversation and Prompts for SMO Registration and

Data Characteristics for this Category

Data for Site Management Organizations (SMO) in registration and declaration document preparation primarily originates from core documents. These include clinical trial protocols, investigator brochures, informed consent forms, ethics committee approvals, and clinical trial reports. These documents update frequently, especially during clinical trials. Protocol amendments, adverse event reports, and progress updates lead to iterations of related materials. Document structures are complex. They typically contain large amounts of unstructured text, tabular data, charts, and scanned images. Fields and units involve medical terminology, dosage units (e.g., mg, mL), time units (e.g., days, weeks, months), biostatistical indicators, and specific format codes required by regulations. Some data may exist as images, such as signature pages or specific diagrams.

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

Frequent updates to SMO documents mean the knowledge base requires frequent index updates. This ensures multi-turn conversations are based on the latest information. Otherwise, the model may cite outdated information. Complex document structures and multiple data types require the Retrieval Augmented Generation (RAG) system to have robust heterogeneous data processing capabilities. This is especially true for image content, which needs effective Optical Character Recognition (OCR) and conversion to searchable text. Identifying and understanding professional fields and units, such as medical terminology and dosage units, is critical for prompt design. Prompts need to clearly instruct the model to identify and extract this specific information, avoiding generalization. Furthermore, strict regulatory compliance requires multi-turn conversations to accurately quote original text when generating content related to declaration documents. Free interpretation is not allowed. This directly influences the setting of parameters like temperature.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`6`	Ensures conversations cover key questions and follow-ups, maintaining contextual coherence.
`Chunk size`	`800–1200 characters`	Balances information density per segment with retrieval efficiency, adapting to long document structures.
`Recall count`	`Top 5–8 entries`	Improves recall rate of relevant information, covering declaration requirements from different angles.
`Similarity threshold`	`0.75–0.85`	Filters low-quality retrieval results, improving answer accuracy.
`temperature`	`0.1–0.3`	Strictly controls model creativity, ensuring responses are based on facts and regulations.
`OCR_ENABLED`	`True`	Processes text information in scanned documents and images, expanding knowledge base coverage.

Three Common Mistakes

AI replies "no answer found": This occurs because image-formatted data in the knowledge base was not effectively OCR-processed, preventing retrieval matching.
AI replies in multi-turn conversations do not align with the latest regulatory requirements: This occurs because the knowledge base index was not updated promptly, leading the model to retrieve outdated materials.
When making API calls, sending questions rapidly and continuously leads to subsequent requests waiting: This occurs due to system concurrency limitations or model response time limits, failing to release resources in time.

How to Verify Configuration

Cross-reference key declaration document entries. Ensure the AI accurately cites the latest version of regulatory documents.
Test questions containing image content. Confirm the AI can correctly identify and extract text information from images.
Simulate multi-turn questions of varying complexity. Observe the logical coherence of conversations under the maxContext setting.
Compare model responses with original documents. Check if the temperature parameter effectively limits the model's degree of free interpretation.

The values provided are common starting points. Measure them 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.