Multi-Turn Conversations and Prompts for Metabolic and

Data Characteristics in This Category

Regulatory submission data in the metabolic and endocrine disease field comes from various sources. These typically include clinical trial reports (e.g., Phase I to Phase III clinical study data), non-clinical study reports (pharmacology and toxicology studies), drug manufacturing process and quality control documents, and published academic literature. Data update frequency correlates closely with drug development progress. Clinical trial data updates periodically, while regulatory guidelines and pharmacopeia standards are revised annually or irregularly. Document structures are highly standardized, often following ICH E3 clinical study report format or CTD format, including detailed sections and subsections. Common fields include subject ID, dosage, observation indicators (e.g., blood glucose mmol/L, HbA1c %, thyroid hormone levels pmol/L), and adverse event codes (MedDRA terms). Precision and consistency of units are critical. For example, blood glucose units often appear as mmol/L or mg/dL and require unified handling.

Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"

The highly standardized and structured nature of metabolic and endocrine data requires multi-turn dialogue systems to accurately identify and extract specific section or field information. For example, when a user asks "What is the primary endpoint of GLP-1 receptor agonist Phase III clinical trials?", the system needs to precisely locate the "primary endpoint" section across multiple clinical trial reports. Unit discrepancies (e.g., blood glucose mmol/L and mg/dL) mean prompt design must consider unit conversion or explicitly state units to avoid ambiguity. The vast volume and hierarchical structure of regulatory submission documents make simple keyword matching insufficient for efficient retrieval, necessitating more complex contextual understanding capabilities. Additionally, the periodic nature of data updates requires the knowledge base to quickly synchronize with the latest clinical data and regulatory requirements. Corresponding prompts must adapt to these dynamic changes, guiding users to query the latest information. Accurate understanding of adverse event codes also requires prompts to guide the AI in terminology mapping or explanation.

Configuration Settings

Configuration Item	Recommended Value	Rationale for This Value
`maxContext`	`6` turns	Regulatory submission processes often require long context tracking. Excessive length dilutes focus. 6 turns can cover most subtasks.
`Chunk size` (Segment Length)	`800–1200 characters`	Paragraphs in clinical trial reports and pharmacology/toxicology reports have high information density. This length helps maintain semantic integrity.
`Recall count` (Recall Count)	`Top 8`	Ensures enough relevant document snippets are recalled for complex queries, covering different data dimensions.
`Similarity threshold` (Similarity Threshold)	`0.75`	Terminology in this domain demands high precision. Increasing the threshold reduces interference from irrelevant content and improves recall accuracy.
`Rerank result count` (Reranked Return Count)	`Top 5`	After reranking, taking the top 5 effectively focuses on the most relevant core information, reducing the model's processing burden.
`Citation Content Template` (Citation Content Template)	Calibrate based on actual measurements	Customize templates for different document types (e.g., clinical reports, pharmacopeia) to ensure key fields are highlighted in citations.

Three Common Mistakes

Dialogue results lack key metric data or have incorrect data units. This happens when the knowledge base indexing does not standardize different units, or prompts do not explicitly request specific units.
API call return results differ significantly from online dialogue, for example, missing certain section content. This happens when the API request parameter detail is set to false, leading to insufficient granularity of returned information, or stream is set to true, causing incomplete parsing of streaming output in some scenarios.
The AI dialogue node misunderstands specialized terminology for specific diseases or drugs, leading to inaccurate answers. This happens when the prompt does not include a domain-specific glossary or alias mapping, preventing the model from correctly recognizing and associating terms.

How to Confirm Correct Configuration

For typical queries (e.g., "pharmacokinetic characteristics of metformin"), test multi-turn conversations to confirm whether key data and units (e.g., Tmax, Cmax) are accurately extracted.
Use different versions of clinical study reports to test whether the system can correctly identify and cite the latest data, verifying the update date field.
Simulate user questions about specific adverse events (e.g., "incidence of hypoglycemia with insulin treatment") to check if the system can recall relevant MedDRA codes and incidence data from clinical safety reports.

The values provided are common starting points and should be measured against the reader's 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.