Data Characteristics in This Domain
Medical affairs quality documentation primarily sources data from pharmaceutical companies' internal R&D reports, clinical trial protocols, registration dossiers, post-market safety reports, medical guidelines and consensuses, SOPs (Standard Operating Procedures), and regulatory documents. These documents typically have a low update frequency. However, updates occur in batches when new drug development, clinical trial approvals, or regulatory revisions happen. Documents are predominantly long-form formal texts, commonly in Word and PDF formats. They contain extensive specialized terminology, abbreviations, charts, and cross-references. Fields and units are highly standardized. Examples include dosage units like mg/kg, time units like hours, days, weeks, and various biological indicator units like ng/mL, IU/L. Documents also include significant structured information such as clinical trial numbers NCTxxxxxx, drug batch numbers LotNo.xxxx, and regulatory clauses GMP Annex 1.
Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts
Long document structures require the model to handle complex contextual dependencies and long-range information associations. This ensures accurate tracing and referencing of specific knowledge points in multi-turn conversations. The dense use of specialized terminology and abbreviations challenges the knowledge base's recall accuracy and the model's semantic understanding, especially with fuzzy queries or non-standard user input. The low update frequency of medical affairs documents means less stringent real-time requirements for knowledge base construction. However, it demands high capabilities for historical version management and traceability, ensuring conversation results are based on specific document versions. The standardized nature of fields and units requires prompt design to guide the model in precisely identifying and extracting numerical information. It also needs to perform unit conversions or comparisons, preventing result deviations due to unit confusion. The presence of cross-references and structured information necessitates that the dialogue system can build knowledge graphs or extract structured information. This allows for quick location of related information in multi-turn conversations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 1000–1500 characters (characters) | Medical documents often contain complete concepts within paragraphs. Too short segments can break semantic integrity; too long increases recall noise. |
Recall count (Recall Count) | Top 8–12 entries (top 8–12 entries) | Ensures sufficient context input to cover multiple knowledge points potentially involved in multi-turn conversations. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | The domain is highly specialized, requiring a higher threshold to ensure recalled results are highly relevant to the query. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5 entries) | Refines the most relevant content through reranking, building on a high recall count, to reduce the model's burden. |
maxContext | 8000–16000 tokens | Accommodates long documents and complex multi-turn conversations, preventing information loss due to context overflow. |
temperature | 0.3–0.5 | Ensures rigorousness and accuracy in responses, reduces hallucinations, and meets the normative requirements of quality documentation. |
Common Pitfalls
- A
422status code or empty response in a conversation usually indicates poor quality retrieved context information due to improper knowledge base chunking strategy, preventing the model from effectively organizing an answer. - The model fails to distinguish between different document versions or data in multi-turn conversations, leading to inconsistent responses. This occurs because the knowledge base lacks a version management mechanism or version information is not used as a key filtering condition during retrieval.
- When users ask for specific values or units, the model's response shows data misalignment or unit errors. This indicates that the prompt did not effectively guide the model to focus on value and unit identification and validation.
How to Verify Configuration
- Select a core quality document and ask multiple questions. Check if the model can accurately cite specific clauses, data, and units from the document and trace them back to the original source.
- Perform fuzzy queries for specialized terms and abbreviations in the document. Observe the accuracy and completeness of the recalled results and check if the model can correctly explain these terms.
- Simulate user queries on the same topic at different times. Verify the model's response to document updates or version changes, confirming it replies based on the latest valid information.
The values provided are common starting points. Measure them against your own samples to determine the most suitable configuration.
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.