High-Value Consumables: Multi-Turn Conversations and Prompts

Data Characteristics for This Category

Data for high-value consumables primarily originates from manufacturer-provided product manuals, registration certificates, clinical application guidelines, technical white papers, and internal training materials. These documents typically exist as PDFs, Word files, or in structured databases. The data update frequency is relatively low, occurring mainly during product iterations, changes in registration regulations, or the publication of new clinical research findings. Document structures are complex, containing extensive specialized terminology, technical parameters, clinical indications, contraindications, usage instructions, adverse reactions, and precautions. Field types are diverse, including product models (e.g., ABC-123X), material compositions (e.g., medical-grade titanium alloy), dimensions (e.g., diameter 3.5mm, length 20mm), sterilization methods (e.g., ethylene oxide sterilization), expiration dates (e.g., 5 years), and compatibility information. Units are precise, such as millimeters, milligrams, and degrees Celsius.

Constraints from These Characteristics on Multi-Turn Conversations and Prompts

The complexity and specialized nature of high-value consumable documents require multi-turn conversation systems to possess robust semantic analysis capabilities for understanding user intent. Low update frequency means less daily maintenance for the knowledge base after initial construction, but the accuracy of initial data entry and validation is critical. Complex document structures and diverse fields necessitate that prompt design explicitly targets information extraction, for example, extracting specific product "indications" or "complications." The presence of specialized terminology requires embedding models to accurately understand industry-specific vocabulary. Precise unit information constrains the model to retain numerical values and units completely when generating responses, preventing information loss or confusion.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`8000`	High-value consumable documents are often lengthy, requiring a larger context window to capture complete information.
`Chunk size` (Segment Length)	`500 characters` (characters)	Ensures each text block contains sufficient product descriptions and technical details while avoiding redundancy from excessive length.
`Recall count` (Recall Count)	`8–12 entries` (items)	Increases the number of recalled items to cover more relevant document segments, improving hit rates for complex queries.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Requires adjustment based on specific data recall performance to ensure results are both relevant and accurate.
`Rerank result count` (Reranked Return Count)	`5 entries` (items)	After reranking, prioritizes the top 5 most relevant results, improving user efficiency in acquiring information.
`temperature`	`0.3`	Reduces the randomness of model-generated answers, ensuring output is fact-based and minimizing hallucinations.

Three Common Pitfalls

During a conversation, Indications field is empty or Material composition is unclear appears: This occurs when the original document parsing fails to correctly identify or extract specific fields, leading to missing information in the knowledge base.
Slow model response speed, long user waiting times: This is due to an excessively large context window, causing the model to process too many input tokens, or insufficient backend model inference resources.
When a user asks for dimensions of product ABC-123X, the model returns specifications of product XYZ-456Y: This happens when the recall strategy fails to precisely match the user-specified product model, or the prompt fails to effectively guide the model to focus on a specific entity.

How to Confirm Proper Configuration

For core product models, conduct multi-turn questioning to verify if the model's answers regarding product features, indications, contraindications, and other key information align with official documentation.
Randomly select over 10 complex queries containing specialized terminology. Check the accuracy and completeness of the recall results, ensuring relevant document segments are effectively retrieved.
Simulate scenarios where users ask follow-up questions about product parameters, usage methods, etc., in different turns. Evaluate if the model can consistently maintain context understanding and provide coherent, accurate responses.
Check key fields for high-value consumables in the knowledge base, such as registration certificate number, manufacturer, and expiration date, to ensure the model can accurately identify and reference this information.

Note: The values provided are common starting points. They should be measured against your own 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.