Multiturn Conversation and Prompts for Stem Cell Therapy

Data Characteristics for This Category

Stem cell therapy registration documents primarily originate from clinical trial reports, non-clinical study reports, manufacturing process files, quality control standards, and relevant regulatory documents. These data documents are typically structured PDF reports containing numerous charts, tables, and specialized terminology. Data update frequency is relatively low, mainly concentrating on clinical trial phase reports and regulatory policy adjustments. Document fields include cell source, preparation process parameters (e.g., culture medium components, passage numbers), quality control indicators (e.g., cell viability, purity, mycoplasma detection), pharmacological and toxicological data, and clinical efficacy indicators (e.g., subject baseline characteristics, treatment effect evaluation criteria, adverse event incidence). Units involve cell counts (e.g., 10^6 cells/mL), concentrations (e.g., ng/mL), time (e.g., weeks, months), and various biological activity units.

Constraints Imposed by These Characteristics on Multiturn Conversations and Prompts

The specialized and complex nature of stem cell therapy submission documents requires the multiturn conversation system to have high-precision semantic understanding and information extraction capabilities. The large number of specialized terms and abbreviations can lead to ambiguity in general models. This necessitates enhancement through specific glossaries and domain knowledge. For tabular and graphical data within documents, the RAG (Retrieval Augmented Generation) process must effectively identify and extract numerical values. The low data update frequency means less need for historical version management in knowledge base construction, but high demand for in-depth analysis and cross-referencing within individual documents. Furthermore, the rigorous logical relationships and citation chains in regulatory documents require the conversation system to trace answers back to specific clauses and support comparative analysis of similar fields across different reports, such as quality control indicator differences between different cell batches.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`6000 tokens`	Ensures sufficient length for submission document snippets and multiturn conversation history, preventing loss of critical information.
`Chunk size` (Segment Length)	`800 characters`	Balances segment granularity with semantic completeness, accommodating common paragraph structures and table sizes in submission documents.
`Recall count` (Number of Retrieved Items)	`Top 8`	Increases the probability of retrieving highly relevant document snippets from the knowledge base, covering more potential associated information.
`Similarity threshold` (Similarity Threshold)	`0.78`	Filters out document snippets highly relevant to the query, reducing interference from irrelevant information and improving answer accuracy.
`Rerank result count` (Number of Reranked Items)	`Top 5`	Further optimizes retrieval results by prioritizing the most relevant snippets, improving AI model processing efficiency.
`maxRetry`	`3 times`	Allows the system to retry a limited number of times when the AI model's generated content does not meet expectations, improving output quality.

Three Common Pitfalls

When chaining multiple AI conversation nodes in a workflow, using the unprocessed intermediate output of a previous node directly as the complete input for the next node. This results in redundant final answers that include unnecessary intermediate reasoning processes.
When configuring a workflow, the HTTP request node does not have correctly set trigger conditions or parameters. This causes some queries to bypass network searches, with the AI model generating answers based on internal knowledge. This leads to insufficient information timeliness.
A CORS error occurs when the frontend requests the conversation API. This is typically due to the backend API not correctly configuring its Cross-Origin Resource Sharing policy, causing the browser to reject loading the request results.

How to Verify Configuration

Conduct multiturn conversation tests on complex questions within specific submission documents. Observe whether answers accurately cite specific field values and clauses from the document, and check if traceability links point to the correct locations.
In the workflow, inspect the input and output of each node via logs or debugging interfaces. Confirm that the output of the preceding AI conversation node undergoes necessary post-processing, and the subsequent AI conversation node receives only refined key information.
Submit queries requiring real-time data. Check if the workflow correctly triggers the HTTP request node and if network search results are effectively integrated into the AI's response.
Use browser developer tools to inspect network requests. Ensure all conversation API requests have a 200 OK status code and no CORS-related error messages.

The values provided are common starting points and should be measured against specific 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.