Multi-turn Conversations and Prompts for Academic Promotion

Data Characteristics for This Category

Academic promotion materials in the biomedical field primarily use data from approved drug inserts, clinical study reports, post-market surveillance data, regulatory policies from drug administrations, industry guidelines, and consensus statements. This information updates frequently, especially clinical research progress and policy adjustments, which can occur monthly or even weekly. Document structures typically include precise medical terminology, dosage units, indications, contraindications, adverse reactions, and strictly adhere to pharmaceutical professional standards. Common file formats include PDF for inserts, Word or Excel for clinical trial data, and structured databases for drug information. Data fields involve drug active ingredients, mechanisms of action, pharmacokinetic parameters, clinical efficacy indicators (e.g., ORR, PFS, OS), statistical P-values, and confidence intervals.

Constraints Imposed by These Characteristics on "Multi-turn Conversations and Prompts"

The rigor of academic promotion materials requires the dialogue system to precisely understand medical terminology and professional data. The system must avoid misinterpretations or incorrect citations. High-frequency data updates mean the knowledge base needs rapid synchronization of the latest information. Failure to do so can lead to outdated or inaccurate answers in multi-turn conversations. Specific fields in document structures, such as dosage units and statistical indicators, require prompt design to guide the model. Prompts must focus on these critical pieces of information and enable accurate extraction and calculation. For instance, when answering drug dosage questions, the model must differentiate adult and pediatric dosages and correctly cite units. Additionally, inquiries about clinical trial P-values in multi-turn conversations require the model to locate and accurately present this information from complex clinical reports. This prevents hallucinations due to insufficient context understanding.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`20000`	Ensures the system accommodates lengthy medical backgrounds and detailed clinical data in multi-turn conversations, maintaining conversational coherence.
`Chunk size` (Chunk Size)	`800–1200 characters` (characters)	Balances text block completeness and retrieval efficiency, preventing truncation of critical medical information.
`Recall count` (Recall Count)	`Top 8 entries` (top 8)	Increases coverage for retrieving relevant clinical data and professional terminology from vast professional documents.
`Similarity threshold` (Similarity Threshold)	`calibrated empirically, e.g., 0.82`	Precisely matches medical terminology and data, avoiding interference from low-relevance content and maintaining professionalism.
`Rerank result count` (Reranked Return Count)	`Top 3 entries` (top 3)	Prioritizes the most relevant and authoritative clinical evidence and regulatory provisions, improving answer accuracy.
`PROMPT_TEMPLATE`	Includes "Please Citation Source Citation page number" (Please cite source document page number)	Forces the model to cite information sources in generated content, enhancing traceability and credibility of academic promotion materials.

Three Common Pitfalls

Citation numbers appear garbled in dialogue output, e.g., [1] becomes [0x01]. This typically occurs due to inconsistent encoding when the underlying model processes special characters, or the frontend rendering fails to parse them correctly.
Expired files cannot be retrieved, preventing the citation of old document content in multi-turn conversations. This happens when the CHAT_FILE_EXPIRE_TIME parameter sets a file lifecycle, and files are purged upon expiration.
AI dialogue returns an empty result in batch execution nodes. This can be due to prompt design that fails to clearly define the responsibilities of each AI dialogue task, making it difficult for the model to effectively distinguish and execute them.

How to Verify Configuration

Conduct multi-turn dialogue tests. Verify the model accurately cites data and units from the latest drug inserts when asked about different dosages and indications.
Monitor token usage in logs. Confirm that maxContext settings are sufficient to support context in complex medical questions and that no semantic drift occurs due to context truncation.
Review the latest clinical study reports in the knowledge base. Ask questions about relevant research results to confirm the system accurately recalls and cites statistical P-values and confidence intervals from the reports, and that the returned content is not garbled.

Note: The values provided are common starting points. Measure them against specific samples to determine optimal settings.

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.