Multi-Turn Conversations and Prompts for SMO Products

Data Characteristics

Site Management Organization (SMO) product data originates from clinical trial protocols, investigator brochures, informed consent forms, Case Report Forms (CRFs), and Standard Operating Procedures (SOPs). This data updates frequently, especially when trial protocols are revised or safety reports are updated. Document structures often include extensive specialized terminology, abbreviations, and diverse formats such as PDFs, Word documents, and Excel spreadsheets. Fields cover medical, pharmaceutical, and statistical domains. Examples include drug dosage (e.g., mg/kg), administration frequency (e.g., times/day), adverse event codes (e.g., MedDRA coding), and visit time points (e.g., weeks/days). Some data contains complex logical relationships, such as dose adjustment rules and inclusion/exclusion criteria.

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

The high update frequency of SMO product data requires the knowledge base to support rapid synchronization and incremental updates. This ensures multi-turn conversations are based on the latest information. Diverse document formats and complex structures mean traditional text segmentation may not capture complete context. This necessitates more refined preprocessing and embedding strategies. Dense specialized terminology and abbreviations challenge the semantic understanding and expansion capabilities of prompts, to avoid bias in responses due to ambiguous terms. Numerical fields with units, such as dosage and frequency, require the model to accurately restate values and perform unit conversions in generated responses, ensuring professional rigor. Complex logical relationships require prompt design that guides the model through multi-step reasoning, to handle complex queries like inclusion/exclusion criteria judgments and dose adjustment recommendations.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`20` turns	SMO consultations involve multi-step reasoning; a longer context helps the model understand information from previous turns.
`Chunk size` (Segment Length)	`800–1200` characters	Balances semantic completeness of specialized documents with retrieval efficiency, avoiding truncation of critical information.
`Recall count` (Retrieval Count)	`Top 5 entries` (Top 5)	Ensures sufficient relevant context is retrieved for reasoning from a large number of knowledge fragments.
`Similarity threshold` (Similarity Threshold)	Calibrated by actual measurement	Requires calibration based on the embedding performance of SMO specialized terminology, to ensure highly relevant documents are retrieved.
`Rerank result count` (Reranked Return Count)	`3` items	Reduces the burden on the model from processing irrelevant information, focusing on the most relevant items for generation.
`Temperature` (`temperature`)	`0.3`	Reduces the randomness of generated answers, ensuring accuracy and consistency of consultation results.

Common Pitfalls

Responses fail to relate to specialized terminology or key information from previous turns, leading to conversational breakdowns. This occurs when maxContext is set too low or Chunk size (Segment Length) is inappropriate, failing to effectively convey context.
The AI provides incorrect units or numerical deviations for drug dosages or visit frequencies. This occurs when prompts do not explicitly instruct the model to focus on and validate numerical units, or when relevant fields in the knowledge base are not specially marked.
When users ask questions involving complex logical judgments within SMO processes, the AI provides vague answers without clear conclusions. This occurs when prompt design fails to guide the model through multi-step reasoning, or when the knowledge base does not effectively extract and organize such logical rules.

Verification of Configuration

Conduct multi-turn conversation tests for typical SMO product queries. Observe if the AI maintains topic consistency and contextual coherence across multiple interactions.
Input queries containing specialized terminology, abbreviations, and numerical units. Verify if these details are accurate and units are correct in the AI's responses.
Simulate user questions about complex logic, such as inclusion/exclusion criteria or dose adjustment rules in trial protocols. Evaluate if the AI can provide logically sound judgments or recommendations.
Review the context citations in the conversation details. Confirm if key information sources are retrieved and if maxContext effectively carries the multi-turn conversation history.

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.