Multi-Turn Conversations and Prompts for IVD Diagnostic

Data Characteristics

IVD diagnostic reagent R&D document data originates from internal R&D platforms, project management systems, and regulatory submission materials. This data includes experimental protocols, raw records, data analysis reports, quality control documents, stability study reports, clinical validation reports, and product inserts. Data update frequency correlates closely with the R&D phase; documents undergo multiple iterations and version updates from project initiation to regulatory approval. Document structures are typically highly standardized, adhering to industry standards like GLP/GMP. They contain clear section headings, figures, tables, appendices, and references. Fields often involve biomarker names, detection methods, reagent components, concentration units (e.g., g/L, mol/L), detection limits (e.g., pg/mL), CV values, lot numbers, and expiration dates. Units are highly standardized, but minor differences may exist between different batches or suppliers.

Constraints on Multi-Turn Conversations and Prompts

The standardized structure of IVD diagnostic reagent R&D documents is critical for designing multi-turn conversations and prompts. The extensive use of specialized terminology and abbreviations in documents requires prompts to accurately identify and contextually link terms, avoiding ambiguity. High data update frequency means the knowledge base must synchronize promptly. The multi-turn conversation system needs to handle version differences, for example, when asked, "What are the stability data differences between lot 20230101 and lot 20221201?" Strict field and unit requirements necessitate precise matching during information extraction. For instance, extracting "detection limit" requires capturing both the numerical value and unit, and handling unit conversion requests. Furthermore, figures and tabular data within documents impose higher demands on RAG (Retrieval Augmented Generation) recall strategies, ensuring multi-turn conversations can effectively index and reference this non-textual information.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192`	IVD document queries often involve significant context, requiring coverage of experimental background, methods, and results.
`Chunk size` (Segment Length)	`500–800 characters` (characters)	Balances semantic completeness of IVD document paragraphs with recall efficiency, avoiding excessive truncation of key information.
`Recall count` (Recall Count)	`Top 8 entries` (top 8)	Ensures coverage of relevant document segments from different experimental stages or dimensions in multi-turn conversations.
`Similarity threshold` (Similarity Threshold)	`0.75`	Guarantees high relevance of recall results to IVD domain query intent, filtering out noise.
`Rerank result count` (Rerank Return Count)	`5 entries` (5 items)	Further optimizes matching with IVD-specific questions by re-ranking recalled results.
`prompt`	Include "IVD Diagnostic Reagent R&D Document Expert" role setting	Guides the model to understand and answer complex IVD-related questions from a professional perspective.

Common Pitfalls

The conversation results include irrelevant intermediate thought processes. This occurs due to improper control over the AI conversation component's output, failing to configure it to output only the final result.
After an API call, the conversation log shows a title but the conversation text is empty. This might be because the API return format does not match expectations, or the model's generated content could not be parsed correctly.
After a user query, the system remains unresponsive for an extended period or displays a "thinking" status. This may be due to PARSE_FILE_TIMEOUT_SECONDS being set too short, causing large document parsing to time out, or due to prolonged recall and reranking times in the RAG process.

Verification Steps

Conduct multi-turn conversation tests for typical IVD R&D questions. Observe if the system accurately extracts and cites specific numerical values from documents (e.g., LOD, CV%) and maintains unit consistency.
Simulate external system calls via the API interface. Check if the returned JSON structure is complete and if key fields (e.g., answer, citations) are populated as expected.
Review conversation logs in the FastGPT backend. Confirm that when processing complex questions, the model does not exhibit intermediate reasoning steps unrelated to the context, and that cited sources are highly relevant to the final answer.

The values provided are common starting points and 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.