Data Characteristics in This Domain
Pharmacoeconomics data primarily originates from clinical trial reports, real-world evidence (RWE), health insurance catalogs, drug pricing documents, and cost-effectiveness analysis studies. Data update frequencies vary. Clinical trial data typically releases after trial completion. Health insurance catalogs and drug pricing information may update quarterly or annually. Document structures are diverse. They include structured clinical study reports (e.g., ICH E3 guidelines), semi-structured literature reviews, unstructured policy documents, and market analysis reports. Field specificity is high. For example, cost data requires differentiation between direct costs (drug procurement, hospitalization fees) and indirect costs (productivity loss). Health outcome indicators may involve specialized units such as Quality-Adjusted Life Years (QALYs) and Incremental Cost-Effectiveness Ratios (ICER).
Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts
The complexity of pharmacoeconomic data sources and inconsistent update frequencies require multi-turn conversation systems to manage and version different data sources meticulously during knowledge base construction. For instance, the system needs to identify and prioritize the latest health insurance payment policy data while retaining historical data for trend analysis. Second, document diversity means prompt design cannot rely solely on a single document parsing model. For structured clinical reports, prompts can directly guide the model to extract specific section information. For unstructured policy documents, greater reliance on context understanding and key information extraction capabilities is necessary. Finally, specific fields and units demand high accuracy in prompts and strong model comprehension. Prompts must clearly define required indicators and units to prevent model confusion regarding concepts like QALYs and ICER, ensuring the economic rationality of pre-screening results.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8192 token | Pharmacoeconomic reports often contain extensive background information and complex reasoning. A longer context window is necessary to maintain conversational coherence and support in-depth analysis. |
Chunk size (Segment Length) | 500 characters (characters) | Ensures each knowledge base segment contains complete economic concepts or data descriptions, while avoiding excessive length that could reduce recall efficiency. |
Recall count (Recall Count) | Top 8 entries (top 8) | Considering the comprehensive nature of pharmacoeconomic analysis, recalling a sufficient number of relevant data snippets is necessary to support multi-dimensional considerations. |
Similarity threshold (Similarity Threshold) | Calibrated by actual measurement (e.g., 0.75) | Based on validation with specific datasets, this balances recall breadth and accuracy, preventing interference from irrelevant information. |
Rerank result count (Reranked Return Count) | Top 4 entries (top 4) | Further filters the most relevant knowledge snippets, reduces model processing load, and highlights core economic data. |
PROMPT_TEMPLATE | Include definitions of key economic indicators | Ensures the model accurately cites or explains the meaning of specialized terms like QALYs and ICER when generating responses. |
Three Common Mistakes
- Missing interaction records for specific time periods in conversation logs. This often results from non-persistent Docker container log configurations or incorrect mapping of log storage paths to the host.
- AI conversation returns empty or incomplete fields after tool invocation. This may stem from prompts not explicitly specifying required tool return fields, or the tool itself failing to parse input parameters correctly.
- Significant differences in response quality between the same prompt on the FastGPT platform and native large model interfaces. This could be due to implicit modifications or enhancements to the original prompt by FastGPT's knowledge base recall or prompt engineering.
How to Confirm Correct Configuration
- Conduct multi-turn conversations for typical pharmacoeconomic pre-screening questions (e.g., "What is the ICER of a new drug?"). Verify the accuracy of data sources, calculation logic, and specialized terminology cited in the model's responses.
- Check FastGPT's embedded tool invocation logs. Confirm all expected tools (e.g., querying cost databases, comparing clinical trial results) are triggered correctly, and their return result structures conform to expectations.
- Use FastGPT's debugging interface to observe the content and quantity of recalled knowledge base segments. Ensure recalled segments contain key data points and contextual information required for pharmacoeconomic analysis, and that similarity scores fall within the set threshold range.
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.