Multi-turn Conversations and Prompts for Academic Promotion

Data Characteristics

Data for academic promotion regulations in the biopharmaceutical sector originates from internal compliance departments and medical affairs departments. This includes regulations, Standard Operating Procedures (SOPs), training materials, and interpretations of relevant laws. Documents are typically in PDF, Word, or internal knowledge base pages. Content is structured, contains specialized terminology, detailed process descriptions, responsibility assignments, and risk advisories. Update frequency is stable, usually annually or quarterly, triggered by policy changes or internal process optimizations. Documents are generally long; an SOP can span dozens or hundreds of pages. Fields and units primarily involve time periods (e.g., days, months), personnel roles, approval levels, and document numbers. Accuracy and consistency are critical.

Constraints on Multi-turn Conversations and Prompts

The length and specialized nature of academic promotion regulation documents require stronger context management in multi-turn conversations to prevent information loss or topic deviation. Extensive specialized terminology and process details necessitate prompt designs that guide the model to accurately understand user intent and reduce ambiguity. The moderate update frequency means the knowledge base update mechanism must support version management, ensuring conversations are based on the latest regulations. The rigorous nature of document content demands that model responses adhere strictly to the source text, avoiding over-inference or generative embellishment, especially in compliance-sensitive scenarios. If a user query involves multiple regulations or SOPs, the model needs to integrate information from multiple sources. This places high demands on retrieval strategies and prompt engineering.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`20000 tokens`	Accommodates long document contexts, preventing loss of key information.
`Chunk size` (Segment Length)	`800 characters` (characters)	Balances semantic completeness and model processing efficiency, reducing slicing loss.
`Recall count` (Number of Retrieved Items)	`Top 10 entries` (top 10)	Covers a broader range of potentially relevant knowledge points, improving multi-turn conversation accuracy.
`Similarity threshold` (Similarity Threshold)	`0.75`	Accurately matches specialized terminology and regulatory clauses, reducing the risk of incorrect retrieval.
`Rerank result count` (Number of Reranked Items)	`Top 5 entries` (top 5)	Focuses on the most relevant regulatory segments, reducing the model's inference burden.
`prompt`	Include instructions like "Strictly answer based on the provided materials, do not make independent inferences."	Ensures compliance and accuracy of answers, preventing the model from generating free-form responses.

Common Pitfalls

The conversation devolves into irrelevant answers or loses context. This occurs when maxContext is set too low, preventing the model from retaining sufficient historical conversation information.
After uploading regulation documents, the model's answers are repetitive or incomplete. This happens when document chapter structures are not considered during slicing, leading to semantic fragmentation.
When a user asks for specific regulatory details, the model provides vague or irrelevant content. This indicates a Similarity threshold (Similarity Threshold) that is too low, retrieving many inaccurate document segments.

Verification Steps

Select multiple complex multi-turn conversation scenarios. Simulate users asking in-depth questions about regulatory details and process steps. Check if the model consistently maintains conversation context and provides accurate answers.
Test the model's ability to answer precisely about different sections of a multi-chapter regulation document. This verifies the effectiveness of the slicing strategy.
Randomly select question-answer pairs from conversation logs. Compare them against the original regulation documents to verify the accuracy and consistency of the model's answers, ensuring no generative deviation.

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.