Multi-turn Conversation and Prompts for Literature-Supported

Data Characteristics

Literature-supported Medical Information (MI) response scenarios primarily use data from global medical journals, conference proceedings, clinical trial reports, and drug inserts. This data updates frequently, especially with new drug approvals or clinical guideline revisions, which lead to intensive publication of relevant literature. Document structures typically follow standard medical paper formats, including abstract, introduction, methods, results, discussion, and references. Key fields include disease name, drug components, mechanism of action, clinical efficacy data (e.g., P value, confidence interval), adverse events, dosage and administration routes, study design type (e.g., randomized controlled trial, retrospective study), publication date, and DOI (Digital Object Identifier). Units involve dosage (mg, g), time (days, weeks, months), concentration (mol/L), and statistical indicators.

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

The high update frequency of literature data requires the knowledge base to rapidly synchronize and index information. This ensures the timeliness of information cited in multi-turn conversations. Standardized document structures help the model understand the semantics of different sections. This allows prompt design to guide the model to extract specific information more accurately, for example, focusing only on data in the "results" section. The precision of key fields like clinical efficacy data means the model must accurately restate or cite original data in its responses. This requires prompts to strictly define output formats and include validation for numbers and units. Metadata such as study design types helps the model assess evidence levels, preventing confusion between different qualities of evidence in multi-turn conversations. Additionally, specialized terminology and abbreviations common in literature require the model to have sufficient domain knowledge understanding or to be supplemented with context via prompts.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Maximum Segment Length`	`500-800 characters`	Balances semantic completeness of segments with recall efficiency, avoiding information redundancy from overly long segments.
`Recall Count`	`Top 5`	Literature-supported MI responses often require citing multiple documents to ensure coverage of primary evidence.
`Similarity Threshold`	`0.75`	Ensures recalled results are highly relevant to the user's question, reducing interference from irrelevant literature.
`Reranked Return Count`	`3`	Further improves the ranking of the most relevant documents based on high relevance, enhancing the quality of the initial response.
`Context Window Size`	`4096 tokens`	Accommodates the complexity and length of medical literature, supporting longer context maintenance in multi-turn conversations.
`System Prompt`	Explicitly requests literature citation, listing DOI or PMID	Ensures the authority and traceability of responses, complying with MI response standards.

Common Pitfalls

Model responses stating "no relevant information" or being overly general. This occurs when the recall count is insufficient or the similarity threshold is too high, failing to retrieve enough relevant literature.
Inability to view historical messages after refreshing conversation records. This is due to improper backend conversation record storage configuration or frontend caching issues, leading to incorrect session ID maintenance.
AI responses containing incorrect data or concepts. This happens when prompts fail to effectively guide the model to extract specific fields from literature, or the model has misunderstandings of medical terminology.

Verification Steps

Conduct multi-turn questioning. Check if the model response accurately cites specific data (e.g., P value, dosage) from the literature and provides document identifiers (e.g., PMID or DOI).
Test conversation functionality at different times. Confirm that historical conversation records persist and load correctly. Check if the session ID is consistent across the frontend and backend.
For specific diseases and drugs, input questions with known answers. Compare the model's output with the original literature content to ensure key information is accurate and medically sound.

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.