Multi-turn Conversation and Prompts for Market Access

Data Characteristics in this Category

Market access quality documents primarily include registration dossiers, clinical trial reports, manufacturing process protocols, quality standards, risk management plans, and post-market surveillance requirements. These documents originate from pharmaceutical companies, medical device manufacturers, CROs (Contract Research Organizations), and national regulatory agencies (e.g., FDA, EMA, NMPA). Update frequency varies from quarterly to annually, or even emergency revisions in special cases, driven by policy changes, product lifecycle stages, and clinical data updates. Document structures are highly standardized, adhering to international standards like ICH, ISO, or national guidelines. They contain extensive structured and semi-structured data, such as product name, generic name, indications, dosage and administration, adverse reactions, quality specifications, test methods, batch numbers, and expiration dates. Units involve milligrams, milliliters, percentages, IU, pH values, etc., with extremely high precision requirements.

Constraints Imposed on Multi-turn Conversation and Prompts

The standardized and specialized nature of market access quality documents imposes strict constraints on multi-turn conversation and prompt design. Documents contain numerous specialized terms and acronyms. The model must accurately understand their meaning in specific contexts, avoiding generalization or misunderstanding. The frequent updates to regulations and guidelines mean the knowledge base needs to stay synchronized; multi-turn conversations may involve interpreting the latest policies. The structured nature of documents allows optimizing conversation flows through precise field extraction. For example, if a user asks "what is the expiration date of drug X?", the system should directly locate the "expiration date" field. Strict requirements for units and precision mean the model must ensure the correctness of values and units when generating responses, without ambiguity or approximation. Complex queries may require combining multiple document fragments and logical inference, which tests the prompt's ability for chained calls and information integration.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	30–50 turns	Ensures the model covers typical market access query context depth and handles complex reasoning.
`Chunk size` (Segment Length)	800–1200 characters	Balances recall granularity with information completeness, adapting to the paragraph structure of quality documents.
`Recall count` (Recall Count)	Top 10–15 items	Increases relevant information coverage, addressing queries with specialized terms and multiple related knowledge points.
`Similarity threshold` (Similarity Threshold)	0.75–0.85	Improves the precision of recall results, reducing interference from irrelevant documents on response quality.
`Rerank result count` (Reranked Return Count)	Top 5 items	Focuses on the most core relevant information, optimizing the conciseness and accuracy of the final response.
`Prompt Template Version`	Calibrated by actual measurement	Ensures consistency with the latest regulations and business processes, with regular iterative optimization.

Common Pitfalls

The conversation model's responses fail to link to the context or are disconnected from the question. This usually happens when the maxContext parameter is set too low, causing the model to lose historical information in multi-turn conversations and fail to form a continuous contextual understanding.
AI nodes in the workflow perform poorly in recognizing specialized terms or phrases containing spaces, leading to inaccurate knowledge recall. This may be due to insufficient consideration of tokenization mechanisms during prompt writing or a lack of proper embedding processing for specialized terms.
The system fails to correctly trigger or terminate loops when processing cyclic generation and judgment logic, resulting in repeated generation of non-compliant text. The cause is often imprecise conditional judgment node logic in the workflow design or insufficiently clear judgment criteria.

How to Confirm Proper Configuration

Conduct multi-turn conversation tests for typical market access queries, such as "What are the latest registration requirements for drug XX in the EU?" Observe whether the model can continuously understand the context and provide relevant and accurate regulatory information.
Use queries containing specialized terms and precise numerical values, such as "Please list the pH value fluctuation range and corresponding units in the test methods for medical device XX." Check if the model can accurately extract and present all key information, including values and units.
Simulate a product declaration document revision scenario. Ask questions involving the integration of information from multiple documents, such as "Compare the differences in clinical trial data requirements for product XX between FDA and NMPA." Confirm that the system can effectively integrate knowledge points from different sources.
By tracking the Recall count (Recall Count) and Similarity threshold (Similarity Threshold) fields in the logs, verify whether the actual number and relevance of recalled document fragments meet expectations, and adjust thresholds based on test results.

The values provided are common starting points. Measure them against specific samples and use cases.

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.