Multi-Turn Conversations and Prompts for Ophthalmology

Data Characteristics

Ophthalmology regulations and SOP documents originate from hospital administration, clinical departments, and pharmaceutical/medical device manufacturers. These documents are typically in PDF, Word, or scanned image formats. Content includes diagnostic and treatment guidelines, surgical procedures, equipment usage instructions, infection control guidelines, and drug management protocols. Updates are driven by policy changes, technological advancements, and clinical feedback, usually occurring quarterly or annually. Document structures are often chapter-based, containing specialized terminology, abbreviations, and diagrams. Common fields include operating steps, risk warnings, scope of application, responsible personnel, and review dates. Units involve time (e.g., minutes, hours), dosage (e.g., mg, ml), dimensions (e.g., mm), and specific device parameters.

Constraints from these Characteristics on Multi-Turn Conversations and Prompts

The specialized nature and complex structure of ophthalmology documents require the model to accurately understand context and professional terminology in multi-turn conversations. Frequent update cycles mean the knowledge base needs regular synchronization to avoid providing outdated information. Diagrams and scanned images within documents can lead to incomplete text extraction, affecting RAG recall accuracy. In multi-turn conversations, users often ask follow-up questions about specific operational details or the relationships between different regulations. This demands the model synthesize information from multiple recalled fragments. For example, regarding the use of a specific ophthalmic surgical instrument, a user might first ask about operating steps, then follow up with cleaning and disinfection protocols. This involves cross-referencing different documents, placing higher demands on maxContext and prompt engineering to ensure information coherence and accuracy.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`500–800 characters`	Balances semantic completeness and recall efficiency, preventing information loss or redundancy from chunks that are too long or too short.
`Overlap Length`	`100–150 characters`	Ensures contextual continuity, reducing semantic breaks at chunk boundaries, especially in procedural documents.
`Recall Count`	`5–8 items`	Balances recall breadth with model processing load, covering multiple related regulations or steps that might be involved in multi-turn conversations.
`Similarity Threshold`	Calibrate by actual measurement	Requires adjustment based on the specific embedding model and dataset to ensure recall relevance and reduce noise interference.
`maxContext`	`3000–4000 tokens`	Allows the model to retain a sufficiently long conversation history, supporting in-depth follow-up questions and detailed inquiries on specialized topics.
`Prompt Template`	Include keywords like "ophthalmology regulations," "SOP," "operating steps," "risk"	Guides the model to focus on specialized knowledge in ophthalmology, improving the accuracy and professionalism of responses.

Three Common Pitfalls

Symptom: The model provides generic answers in conversations instead of citing knowledge base content. Reason: Similarity Threshold is set too high, filtering out relevant recall fragments with slightly lower relevance scores.
Symptom: When a user asks a follow-up question about the next step in a procedure, the model's answer is fragmented or repetitive. Reason: Chunk Length is too small, breaking up procedural descriptions. maxContext is insufficient to carry the complete conversation context.
Symptom: After uploading PDF documents, some diagrams or scanned content cannot be retrieved. Reason: The document parsing component does not effectively process non-text content. These documents require preprocessing or manual annotation.

How to Verify Configuration

Select core ophthalmology regulation documents and conduct multi-turn questioning. Check if the answers accurately cite original knowledge base links or paragraphs.
Simulate a user asking continuous questions about a complex surgical procedure. Evaluate the model's logical coherence and ability to supplement information between different steps.
Randomly select professional terms from the knowledge base and ask questions. Verify if the model's explanation of the terms aligns with the original definition in the regulations.
After a knowledge base update, test whether the model promptly reflects the latest regulatory content and provides answers based on the new regulations for previous questions.

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.