Data Characteristics
Compliance script data in the biomedical field originates from regulatory documents, industry guidelines, internal training materials, and anonymized historical consultation records. Data typically exists as PDFs, DOCX files, or plain text. Content structure varies, often containing specialized terminology, legal clauses, and clinical guidelines. Updates occur quarterly or annually, driven by policy changes and industry dynamics. Emergency updates can happen with significant policy shifts. Documents frequently include fields like "Article N of the Drug Administration Law," "XX Guidance (2023 Edition)," and "Scope of Indications." Units are often dates, chapter numbers, or specific medical parameters. The data volume is large and fragmented.
Constraints on Multi-Turn Conversations and Prompts
The highly specialized and rigorous nature of compliance script data demands precise and authoritative answers in multi-turn conversations. Misleading information must be avoided. The update frequency of regulatory documents requires robust synchronization and version management mechanisms for the knowledge base. This ensures the use of the latest effective clauses. Complex document structures, including nested information and cross-references, impose higher demands on prompt construction. Prompts must guide the model to accurately extract key information and establish logical connections. The presence of specialized fields and units requires the model to identify and correctly parse context when understanding user queries, for example, distinguishing between drug batch numbers and production dates. During conversations, the model must also identify user intent, determine if questions involve sensitive or prohibited topics, and provide compliant guidance or refuse to answer.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Balances the completeness of regulatory provisions with model processing efficiency. |
Recall count | Top 8 entries | Increases coverage of relevant regulatory clauses and reduces omissions. |
Similarity threshold | 0.75 | Ensures strong relevance of retrieved content and filters out noise. |
Rerank result count | Top 3 entries | Selects the most core regulatory content based on high recall. |
maxContext | 3000 tokens | Accommodates the accumulated context information in multi-turn conversations. |
temperatur | 0.3 | Reduces model divergence and ensures strict, compliant answers. |
Common Mistakes
- Frequent citation of irrelevant regulatory clauses during conversations indicates an unreasonable knowledge base segmentation strategy, leading to fragmented semantic units.
- AI answers contain outdated regulatory information. This occurs when the knowledge base fails to synchronize with the latest policy documents or when version management mechanisms are ineffective.
- The AI cannot accurately identify and provide relevant traceability information when users ask about "drug batch numbers." This happens when prompts do not sufficiently guide the model to recognize and parse specific fields.
Verification of Configuration
- Randomly select 10 recently published regulatory questions. Verify if the AI's answers cite the latest and correct regulatory clauses.
- Simulate 5 sets of multi-turn conversations involving specialized terminology and ambiguous intent. Observe if the AI accurately understands and provides compliant guidance.
- Check the version records of regulatory documents in the knowledge base. Ensure all key policy document update dates match official release dates.
The values provided are common starting points and should be measured against specific 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.