Multi-turn Conversation and Prompting for Structured

Data Characteristics

Patient aid program data primarily originates from pharmaceutical company R&D reports, clinical trial documents, drug prescribing information, patient education materials, and compliance review records. These documents are typically in PDF format and contain extensive unstructured text, tables, and figures. Update frequency depends on new drug development progress, clinical data releases, and regulatory policy adjustments, usually quarterly or annually. Some critical clinical data may update monthly. Document structures are complex. For example, a clinical trial report might include sections on research protocols, ethical approvals, subject screening, medication records, adverse event reports, and statistical analysis results. Fields and units are highly specialized, covering pharmacology (e.g., PK/PD parameters, Cmax in ng/mL), medical statistics (e.g., P value, CI interval), and dosimetry (e.g., mg/kg), often using abbreviations.

Constraints on Multi-turn Conversation and Prompting

The complexity and specialized nature of patient aid R&D documents impose specific requirements on multi-turn conversation context management and prompt design. The documents contain many specialized terms and abbreviations, requiring the model to accurately understand and maintain contextual consistency. This prevents conversation interruptions or deviations due to ambiguous terminology. In multi-turn conversations, users may progressively refine query conditions. For example, a user might go from "clinical efficacy of a certain drug" to "ORR data for this drug in specific genotypes of patients." This requires the system to track and integrate historical conversation information and dynamically adjust retrieval strategies. The uncertain document update frequency means indexes require regular maintenance to ensure information timeliness. Additionally, due to patient data and compliance considerations, prompts must guide the model to emphasize information sources and credibility in its responses and avoid generating any content that suggests diagnosis or treatment advice.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`2048` Token	Balances context length for complex queries with model processing efficiency, preventing frequent truncation.
`Chunk size` (Segment Length)	`800` characters	Ensures each text segment contains sufficient semantic information, handling dense specialized terminology.
`Recall count` (Retrieval Count)	`10` entries	Covers more potentially relevant document snippets, improving recall for complex queries.
`Similarity threshold` (Similarity Threshold)	`0.75`	Increases matching precision for specialized documents, reducing interference from irrelevant content.
`Rerank result count` (Reranked Return Count)	`5` entries	Selects the most relevant snippets to user intent, enhancing accuracy in multi-turn conversations.
`PARSE_FILE_TIMEOUT_SECONDS`	`600` seconds	Handles parsing of large PDF documents, preventing indexing failures due to parsing timeouts.

Common Pitfalls

Symptom: The model repeatedly asks for information previously provided in the conversation. Reason: The maxContext parameter is set too low, or the context management strategy is inadequate, leading to truncation of information from earlier conversation turns.
Symptom: When a user queries a specific metric, the model's answer does not include a link to the original source, or it states "no relevant information found." Reason: The Recall count (Retrieval Count) or Similarity threshold (Similarity Threshold) is set incorrectly, failing to retrieve enough source document snippets or to correctly link to the original source.
Symptom: The model responds normally at the beginning of a conversation, but after refreshing the page, it displays "no available index model detected." Reason: The index building task failed to complete successfully or the index expired. Check the index status and update mechanism.

Validation Steps

Select typical complex queries from patient aid projects and conduct multi-turn conversation tests. Verify if the model accurately understands and progressively refines user intent.
For multiple specialized terms and abbreviations, verify if the model maintains consistent understanding throughout the conversation and correctly identifies their meaning within the documents.
Randomly select key data points from documents. Ask questions to verify if the model can accurately extract them and provide corresponding sources. Check if the links in the Source field are valid.
Simulate a document update scenario. After uploading a new version of a document, verify if the model's responses reflect the latest information. Check the Last Updated Time field.

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.