Data Characteristics for this Category
In the biopharmaceutical industry, CSO (Contract Sales Organization) regulations and SOP documents are typically in PDF, Word, or internal knowledge base page formats. These documents cover sales processes, compliance requirements, product knowledge, and performance evaluation standards. They are lengthy, complex in structure, and contain extensive specialized terminology and cross-references. Update frequency often aligns with changes in policies and regulations, product life cycles, or internal process optimizations, potentially quarterly or semi-annually. Documents frequently include fields such as drug names, disease codes, sales region codes, and compliance approval numbers. Units involve monetary amounts, time periods, and quantities.
Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"
The complexity of CSO regulatory documents requires a multi-turn conversation system with robust context understanding to process related information across documents and sections. The length and high specialization mean a single prompt may not fully capture user intent, necessitating gradual refinement through multi-turn questioning. Document update frequency dictates that the knowledge base synchronization mechanism must be efficient, ensuring answers are based on the latest regulations. The presence of specialized terminology and codes requires the system to accurately identify and link to internal knowledge, preventing incorrect answers due to misinterpretation of terms. Furthermore, compliance is a core requirement; answers must strictly adhere to the original text and avoid over-inference or interpretation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 6 turns | CSO regulatory Q&A often requires a longer context to understand complex questions and follow-ups. 6 turns cover most scenarios. |
temperature | 0.3 | Regulatory Q&A prioritizes accuracy and consistency. A lower temperature value helps reduce model creativity, maintaining the rigor of responses. |
systemPrompt | See below | Explicitly instructs the model to strictly answer based on documents and emphasizes compliance. |
recallTopK | 10 items | Regulatory documents have high content density. Increasing the number of recalled items helps cover more relevant fragments, reducing omissions. |
rerankTopN | 3 items | After recalling multiple fragments, re-ranking selects the most relevant ones for generation, improving answer quality. |
chunkSize | 800 characters | Paragraphs in regulatory documents are often long. A chunkSize of 800 characters helps maintain semantic integrity, preventing critical information from being split. |
Three Common Mistakes
- Symptom: After a user asks a question, the model replies "Unable to find relevant information" or provides a generic answer. Reason: Incorrect
recallTopKorchunkSizesettings, leading to relevant knowledge not being recalled or recalled fragments being incomplete. - Symptom: The model "forgets" previous questions or context in a multi-turn conversation, and answers deviate from the topic. Reason:
maxContextis set too low, causing conversation history to be truncated and the model unable to maintain complete conversational context. - Symptom: API calls return a
401 Unauthorizederror, or logs show an invalid token. Reason:ONEAPI_KEYis configured incorrectly or has expired, preventing FastGPT from properly calling upstream model services.
How to Confirm Correct Configuration
- Perform multi-turn Q&A tests for typical CSO regulatory questions. Verify the accuracy, completeness, and contextual coherence of the answers.
- Check FastGPT's log output to confirm that the knowledge base recall fragments are highly relevant to the user's question and that the
rerankTopNselected fragments are of high quality. - Simulate a regulatory update, upload the new version of the document, and ask related questions. Confirm that the system can answer based on the latest knowledge.
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.