Data Characteristics
Ophthalmic quality documentation includes clinical trial protocols, investigator brochures, adverse event reports, SOPs (Standard Operating Procedures), and regulatory compliance documents. Data primarily comes from structured or semi-structured documents submitted by internal R&D departments, clinical research organizations, and external CRO companies. Update frequency depends on clinical project progress and regulatory changes. Major revisions typically occur during different phases of clinical trials or regulatory updates, with minor corrections and additions happening routinely. Document structure usually adheres to regulatory requirements such as ICH GCP (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Good Clinical Practice) or NMPA (National Medical Products Administration), featuring clear chapter divisions and numbering systems. Fields cover drug dosage, administration routes, subject inclusion/exclusion criteria, follow-up periods, intraocular pressure measurement units (mmHg), and visual acuity correction units (Snellen fraction or LogMAR). The precision of units is critical for medical judgment.
Constraints on Multi-turn Conversations and Prompts
The specialized and rigorous nature of ophthalmic quality documentation requires multi-turn dialogue systems to precisely identify medical terminology and units during user queries, preventing semantic drift. The dynamic nature of documentation means the knowledge base needs regular incremental updates to ensure the timeliness and accuracy of dialogue results. Strict structural requirements necessitate that RAG (Retrieval Augmented Generation) considers contextual relevance when recalling related passages. For example, an adverse event report may need to link to its corresponding clinical trial protocol and SOP. In multi-turn conversations, users might ask in-depth questions about specific ophthalmic diseases (e.g., glaucoma, cataracts) or drugs (e.g., ocular hypotensive agents). This requires prompt design to guide the model in professional domain reasoning and to accurately understand unit conversions and numerical ranges. For queries about key indicators like visual acuity and intraocular pressure, the model must extract and compare data from different document formats.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2000–3000 characters | Ensures that complex ophthalmic questions and the background information needed for model responses are covered in multi-turn conversations. |
Chunk size (Segment Length) | 300 characters | In ophthalmic documents, key information is often concentrated in short paragraphs. Smaller segments improve recall precision. |
Recall count (Number of Retrieved Items) | Top 5–8 items | Given the specialized nature of ophthalmic documents, increasing the number of retrieved items enhances coverage and helps avoid missing critical medical terms or data. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Raising the threshold ensures that retrieved document segments are highly relevant to ophthalmic queries, reducing interference from irrelevant information. |
Rerank result count (Number of Reranked Items) | Top 3 items | After reranking, taking a small number of the most relevant segments for generation reduces the model's processing burden and improves response speed. |
History Message Limit | 10 turns | Considering that ophthalmic questions may require a longer context history, appropriately increase the history message limit. |
Three Common Pitfalls
- Symptom: The model frequently responds with "Insufficient information, cannot answer" during conversations. Reason: The
Similarity threshold(Similarity Threshold) is set too high, preventing relevant information from being recalled even when present. - Symptom: When users ask about intraocular pressure unit conversions or drug dosages, the model provides incorrect values or units. Reason: The prompt does not explicitly require the model to focus on the accuracy of values and units, or the extraction and standardization of relevant data in the knowledge base is insufficient.
- Symptom: In multi-turn conversations, the model fails to connect context and "forgets" previously provided ophthalmic symptoms or drug information. Reason:
maxContextorHistory Message Limitis set too low, preventing the model from retaining a sufficiently long conversation history for reasoning.
How to Verify Configuration
- Ask multi-turn questions about core issues such as diagnostic standards, treatment plans, and drug contraindications for specific ophthalmic diseases. Check if the model accurately cites relevant regulations or clinical guidelines.
- Input queries containing different measurement units (e.g., intraocular pressure in mmHg, visual acuity in LogMAR). Verify if the model correctly identifies, cites, or converts these units in its answers.
- Simulate actual inspection scenarios by posing complex questions related to compliance requirements. Observe if the model can link multiple documents and provide logical, well-supported answers. Then, compare these answers with expert opinions and set an acceptable threshold.
Note: 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.