Multiturn Conversations and Prompts for Structured Analysis

Data Characteristics in This Domain

Biopharmaceutical regulatory submission documents often contain large amounts of unstructured and semi-structured data. Examples include Clinical Study Reports (CSRs), non-clinical study reports, Chemistry, Manufacturing, and Controls (CMC) documents, drug labels, and Common Technical Document (CTD) modules. These documents come from various sources, including internal R&D teams, Contract Research Organizations (CROs), and external partners. Data updates are frequent during late-stage R&D and the submission phase, especially during deficiency responses and review feedback. Document formats are primarily PDF, Word, and Excel, containing numerous tables, figures, and lengthy narratives. Fields and units are highly specialized. For example, pharmacokinetic parameters (AUC, Cmax, Tmax, with units ng·h/mL, ng/mL, h), toxicology indicators (LD50, with unit mg/kg), and clinical efficacy endpoints (ORR, PFS, with units %, months) are common, often accompanied by complex medical terminology and abbreviations.

Constraints Imposed by These Characteristics on Multiturn Conversations and Prompts

The specialized nature, diversity, and complex structure of regulatory submission documents demand high accuracy and robustness from multiturn conversations and prompts. First, the unique professional terminology and abbreviations in the documents require prompt design to fully incorporate domain-specific vocabularies, preventing model misunderstanding. Second, the extensive table and figure data mean simple text parsing is insufficient for extracting key information. Prompts must guide the model to perform structured data extraction and understand data relationships. Furthermore, the rigorous nature of submission documents requires accurate model output. Any hallucination or misinterpretation could lead to severe consequences. Therefore, prompts must emphasize fact-checking and traceability capabilities. Multiturn conversations need to handle user follow-up questions on specific data points, regulatory clauses, or test results, requiring the model to maintain context and precisely locate information within vast documents.

Configuration Settings

Configuration Item	Recommended Value	Rationale for This Value
`maxContext`	`8000 tokens`	A single query for regulatory submission documents may involve multiple related paragraphs, requiring a longer context window to maintain semantic coherence and prevent loss of critical information.
`temperature`	`0.3`	Aims for accuracy and consistency in responses, reducing the model's tendency to generate random or divergent content, ensuring reliable output.
`top_p`	`0.5`	Further limits model generation diversity, focusing it on high-probability tokens, reducing uncertainty, and meeting the rigor required in professional fields.
`rerank_enabled`	`true`	When retrieving from a large volume of documents, initial recall may contain noise. Reranking can prioritize the most relevant document segments.
`prompt_template`	`Refer to Appendix A`	Must include domain vocabulary guidance, structured information extraction instructions, and traceability requirements to ensure the model understands the intent and outputs according to specifications.
`max_turn_limit`	`10 Turns`	Considering users may ask multi-level follow-up questions about submission details, a reasonable turn limit supports in-depth exploration while preventing infinite loops.

Three Common Mistakes

Frontend requests encounter CORS cross-origin errors, preventing conversations from starting. This typically happens when the backend API does not correctly configure the Access-Control-Allow-Origin header, disallowing access from the frontend domain.
The response content from a previous AI conversation in a workflow is used as input for a second AI conversation, but the final result outputs the entire content of the first AI conversation. This indicates incorrect output filtering or mapping configuration between workflow nodes, failing to correctly extract or truncate intermediate results.
The model directly answers questions without triggering configured HTTP requests or web searches in the workflow. This may be due to an unclear prompt design that fails to effectively guide the model to identify the intent for external tool calls, or the intent recognition threshold is set too high.

How to Verify Correct Configuration

Simulate queries containing professional terminology and abbreviations to check if the model accurately understands and applies domain-specific vocabulary, without obvious misinterpretations or hallucinations.
Ask questions involving data comparison or calculations based on tabular data in documents. Verify if the model can correctly extract and understand structured data, and if the output matches the original data.
In a multiturn conversation, ask in-depth follow-up questions on the same topic. Observe if the model maintains contextual coherence, synthesizes information from different document segments to provide answers, and offers information traceability.

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.