Multiturn Conversation and Prompts for Structured Analysis

Data Characteristics for This Category

R&D document data for imaging equipment, such as CT, MRI, and ultrasound diagnostic devices, primarily originates from internal R&D processes. Sources include design specifications, test reports, user manual drafts, software module descriptions, and hardware schematic descriptions. These documents are typically stored in formats like PDF, Word, and Markdown. They are frequently updated, especially during product iterations or software upgrades. Document structures are complex, containing extensive specialized terminology, technical parameters, performance indicators, and operational procedures. Fields include unique units and expressions such as spatial resolution (lp/cm), signal-to-noise ratio (dB), scan time (ms), and image matrix (pixels). Some documents also embed images and charts to illustrate equipment structure or test results.

Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts

The highly specialized nature and structural complexity of imaging equipment R&D documents impose specific requirements on the accuracy of multiturn conversations and prompt design. First, the extensive technical parameters and abbreviations in the documents demand strong semantic understanding from the model to avoid conceptual confusion during multiturn interactions. For example, MR can refer to magnetic resonance or medical report, making contextual recognition crucial. Second, high document update frequency means the knowledge base requires frequent synchronization; the model must handle the latest version information. Third, traditional text parsing struggles to extract content from embedded charts and images, which can lead to missing information in conversations. Finally, unique fields and units, such as kVp (kilovolt peak) or mA (milliamperes), require prompts to guide the model in correctly extracting and interpreting values, and to prevent unit conversion errors.

Configuration Settings

Configuration Item	Recommended Value	Rationale for the Value
`maxContext`	`6`	R&D questions often require extended context tracing. Six conversation turns cover most technical detail discussions, avoiding repetitive questioning.
`Chunk size` (Segment Length)	`800–1200 characters`	Imaging equipment document paragraphs are often long and contain complex technical descriptions. Longer segment lengths help maintain semantic completeness and reduce context loss due to splitting.
`Recall count` (Recall Count)	`Top 8 entries`	R&D documents are knowledge-intensive. Increasing the recall count improves the model's probability of retrieving relevant information, addressing multi-faceted questions, especially when multiple modules or parameters interact.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Given the precision requirements for specialized terminology, a higher similarity threshold helps filter out more accurate matching results, reducing irrelevant information interference, especially when dealing with similar but subtly different technical specifications.
`Rerank result count` (Reranked Return Count)	`Top 5 entries`	After recalling multiple documents, reranking to select the top 5 further focuses core information, improving conversation accuracy, especially for in-depth user inquiries about specific parameters or design solutions.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Imaging equipment R&D documents are typically large and complex, requiring significant parsing time. Increasing the timeout ensures large documents can be fully processed, preventing parsing failures due to timeouts.

Common Mistakes

Symptom: After a user query, the AI's response content does not match actual document information, or even contains incorrect technical parameters. Reason: During document parsing, critical data from charts and images were not effectively identified and extracted, leading to incomplete knowledge base information.
Symptom: An HTTP request was configured in the workflow for online searching of the latest standards, but the AI did not trigger this request when answering, instead using internal knowledge base information directly. Reason: Prompt design failed to clearly guide the AI to prioritize calling external HTTP tools in specific scenarios (e.g., when asking about the latest industry regulations).
Symptom: The conversation interface returns a CORS policy error code. Reason: After private deployment, the frontend request domain did not match the backend service domain, and the backend was not correctly configured for Cross-Origin Resource Sharing (CORS) policy.

Verification of Configuration

Select an imaging equipment test report containing complex technical parameters (e.g., MTF curves, DQE values). Conduct multiturn questioning to verify if the model can accurately extract and explain the meaning and values of these parameters.
For frequently updated design specifications or software version information in documents, simulate user queries. Check if the model correctly cites the latest version of the document content and states the information source.
Write specific prompts that require the model to synthesize information from different documents (e.g., hardware design documents and software interface documents) for comprehensive answers. Verify if the logical consistency of the answers meets expectations, especially for parts involving cross-module collaboration.

Note: The values provided are common starting points. Measure performance 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.