Data Characteristics in This Category
Ophthalmic regulatory submission documents cover diverse types, including preclinical study reports, clinical trial protocols, investigator brochures, informed consent forms, marketing application forms, package inserts, and labels. Data sources are varied. These include clinical trial reports from Contract Research Organizations (CROs), patient follow-up data from hospital Electronic Medical Record (EMR) systems, internal research and development documents from pharmaceutical companies, and regulatory documents and guidelines from the National Medical Products Administration (NMPA).
Data update frequencies vary. Regulatory documents typically update quarterly or annually. Clinical trial data generates in real-time as trials progress. Product inserts remain relatively stable after approval. Document structures are complex. They often contain numerous charts, statistical data, and specialized terminology. Examples include intraocular pressure (IOP) measurements, visual acuity (VA) scores, OCT imaging reports, and fundus photographs. Fields and units are highly specific. Visual acuity often expresses as Snellen fractions or LogMAR values. IOP units are mmHg. Visual field test results show in dB values.
Constraints Imposed by These Characteristics on Multiturn Conversations and Prompts
The complexity and specialization of ophthalmic regulatory submission data impose specific requirements on multiturn conversation and prompt design. First, diverse data sources mean the system must flexibly switch between different knowledge bases or data sources during a conversation. This ensures comprehensive information. For example, discussing clinical trial results may require referencing clinical trial reports. Discussing regulatory requirements may necessitate retrieving NMPA guidelines.
Second, documents contain charts and image data, such as OCT images. These cannot render directly through text conversations. The system must provide corresponding file links or summary descriptions in the conversation. Third, highly specific fields and units, such as LogMAR visual acuity values, require prompts to accurately identify and process these specialized terms. This avoids misunderstanding or incorrect conversion. Finally, the update frequency of regulatory documents and the dynamic nature of clinical data necessitate version management capabilities. This ensures conversations base on the latest, most accurate information.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Ensures enough capacity for longer contextual information in ophthalmic professional documents. This facilitates understanding complex clinical trial designs and regulatory terms. |
Chunk size (Segment Length) | 800–1200 characters | Balances contextual coherence and retrieval efficiency. This avoids information redundancy from overly long segments or semantic fragmentation from overly short ones. |
Recall count (Recall Count) | Top 10 | Increases coverage of relevant knowledge points. This improves information recall, especially when dealing with interdisciplinary issues. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements, 0.75 suggested | Guarantees the precision of recalled content. This prevents interference from irrelevant or low-relevance information while maintaining some generalization capability. |
Rerank result count (Reranked Return Count) | Top 5 | Refines the key information presented to the user. This reduces user reading burden and focuses on core issues. |
temperature | 0.3 | Reduces the randomness of model responses. This ensures accuracy and consistency in rigorous scenarios like regulatory and clinical data interpretation. |
Three Common Mistakes
- The conversation displays numerous special characters like
#*. This might occur if an external system (e.g., Feishu or WeChat) has incompatible Markdown parsing, causing raw Markdown tags to render directly. - Calling the conversation interface does not return the referenced knowledge base ID. This often results from an interface design or configuration issue, where the backend does not return knowledge base metadata, preventing traceability of information sources.
- The knowledge base contains image URLs, but the model cannot output images in the conversation. This might be because the model itself lacks direct image rendering capabilities, or the conversation interface does not integrate image preview functionality.
How to Confirm Proper Configuration
- Conduct multiturn conversation tests using a set of typical ophthalmic regulatory submission questions. Check if the model consistently maintains contextual coherence and guides the conversation progressively based on question depth.
- During the conversation, randomly select several key professional terms or data points. Verify if the model's understanding and referencing of this information, especially units and numerical values, align with the original documents.
- Simulate user questions about the latest regulations or clinical developments. Observe if the model references the latest version of documents in the knowledge base and provides clear version information.
The values provided are common starting points. Measure against your own 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.