Multi-turn Conversations and Prompts for Solid Tumor R&D

Data Characteristics

Solid tumor R&D documents include clinical trial protocols, investigator brochures, pathology reports, imaging reports, gene sequencing data, and drug mechanism of action literature. Data sources are diverse, ranging from structured tables to extensive unstructured text like physician notes and patient interviews. Updates are frequent, especially during clinical trials, where protocol amendments, patient enrollment, and adverse event reports continuously generate new data. Document structures are complex, often containing multi-level headings, figures, and appendices. Fields and units are highly specialized, for example, tumor staging (TNM), pathological types (adenocarcinoma, squamous cell carcinoma), gene mutation sites (EGFR L858R, KRAS G12C), drug dosages (mg/kg), and efficacy evaluation criteria (RECIST 1.1). Accurate identification of these terms and units is critical for subsequent analysis.

Constraints on Multi-turn Conversations and Prompts

The complexity of solid tumor R&D documents places high demands on multi-turn conversation and prompt design. First, documents contain numerous specialized terms and abbreviations. Prompts require strong domain knowledge to avoid parsing errors due to lexical ambiguity. Second, frequent document updates necessitate rapid knowledge base synchronization to ensure data consistency across multi-turn conversations. Third, multi-level structures and mixed data types mean multi-turn conversations must guide users to clarify query scope, for example, whether to query clinical data or mechanisms of action, or whether to target specific pathological types or gene mutations. Finally, the precision required for fields and units means prompts must accurately extract and present numerical information, such as ensuring correct dosage units to prevent result discrepancies from unit confusion.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`3000` characters	Solid tumor documents have strong contextual relevance. A longer conversation history maintains coherence.
`Chunk size` (Segment Length)	`500–800` characters	Balances the specialized nature and information density of solid tumor documents, ensuring semantic completeness per segment.
`Recall count` (Recall Count)	`Top 8–12 entries` (Top 8–12 items)	Solid tumor information involves multiple dimensions. Increasing recall count improves relevance coverage.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Ensures recalled document segments are highly relevant to solid tumor queries, reducing interference from irrelevant information.
`Rerank result count` (Rerank Return Count)	`Top 5 entries` (Top 5 items)	After high recall, reranking focuses on the most critical solid tumor-related information.
`MAX_RESPONSE_TOKENS`	`2048` Tokens	Ensures complete output for complex solid tumor query results, preventing truncation of critical medical information.

Common Pitfalls

Calling the conversation question guidance interface results in an unAuthChat error. This typically indicates an incorrect API Key configuration or insufficient permissions, preventing the system from validating the session.
AI conversation responses contain newline characters, causing subsequent JSON parameter parsing to fail. This occurs when the model output does not strictly adhere to JSON format requirements, or the parser does not handle special characters.
In multi-turn conversations, the AI cannot accurately answer questions related to specific gene mutation treatment plans for solid tumors. This manifests as generic information or an inability to retrieve specific drugs. This happens when the knowledge base lacks such fine-grained information or segmentation fails to effectively retain key associated details.

Validation Steps

Conduct a series of multi-turn conversation tests covering solid tumor diagnosis, treatment plans, and prognosis. Verify the AI maintains context understanding across different turns.
Query structured information, such as specific drug dosages and adverse event rates, from typical solid tumor clinical trial documents. Check the accuracy and completeness of the returned results.
Simulate user questions about solid tumors that include specialized terms and abbreviations. Check if the AI correctly identifies them and provides relevant document citations. Confirm the Similarity threshold (Similarity Threshold) is appropriate.
Use solid tumor research reports containing complex tables and figures for Q&A. Observe if the AI can extract key numerical values and conclusions from unstructured text. Verify the correctness of units for extracted fields.

The values provided are common starting points. Measure them against your 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.