Data Characteristics
Data for pharmaceutical data distribution primarily originates from pharmaceutical companies and CROs (Contract Research Organizations). This includes clinical trial reports, drug inserts, research papers, and industry guidelines. Updates are irregular, with concentrated releases during new drug launches or clinical data publications. Document structures are mainly text-based, often in PDF, Word, or structured databases like PubMed. These documents contain extensive specialized terminology, dosage units (e.g., mg/kg), statistical indicators (e.g., p-value, confidence interval), compound names, and trial phase information. The data volume is large and complex, often requiring preprocessing to extract key information.
Constraints on Multi-turn Conversation and Prompts
The specialized and complex nature of the data requires multi-turn conversations to precisely understand user intent. The system must recall relevant snippets from a vast and structurally diverse document set. Specialized terminology and abbreviations demand strong semantic understanding from the model to prevent misinterpretations. The precision of details like dosages and statistical data means prompt design must guide the model to focus on correct values and units, avoiding vague responses. Irregular document updates necessitate that the RAG system's knowledge base quickly synchronizes with the latest information, ensuring multi-turn conversations are based on current sources. Additionally, users may repeatedly inquire about specific metrics or trial results, requiring the system to maintain conversational context and efficiently retrieve and integrate information in subsequent turns.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 Tokens | Accommodates longer user queries and system responses while maintaining performance. |
Chunk size (Segment Length) | 500 characters (characters) | Adapts to the longer paragraphs and high information density typical of biomedical documents, ensuring segment completeness. |
Recall count (Recall Count) | Top 8 entries (top 8) | Expands the recall scope to cover more potentially relevant specialized information, reducing omissions. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall accuracy and recall rate, filtering out irrelevant document snippets. |
Rerank result count (Rerank Return Count) | Top 3 entries (top 3) | Refines the final context presented to the model while maintaining relevance, improving efficiency. |
queryExtension | Enabled (Enabled) | Automatically expands query terms for specialized terminology and abbreviations, enhancing retrieval accuracy. |
Common Pitfalls
- Symptom: During multi-turn conversations, the model "forgets" previous questions and cannot link them. Reason: The
maxContextparameter is set too low, truncating historical conversations and causing the model to lose context. - Symptom: When a user asks about a specific drug dosage, the model provides inaccurate values or units. Reason: The prompt failed to explicitly guide the model to focus on the precision of values and units, or key numbers and units were separated during document segmentation.
- Symptom: The system responds slowly, especially with complex queries. Reason: The
Recall count(Recall Count) is set too high, leading to an excessive amount of data processed during the retrieval and reranking stages, increasing latency.
Verification
- Conduct multi-turn conversation tests with varying complexities of specialized questions. Check if the model accurately understands intent and consistently tracks context.
- Randomly select key data points from the knowledge base (e.g., drug dosages, p-values). Ask questions to verify if the model can accurately cite this information during conversations.
- Simulate user queries after data updates. Confirm the system provides answers based on the latest version of the data to verify the effectiveness of the knowledge base synchronization mechanism.
Note: The values provided are common starting points. Measure performance 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.