Data Characteristics in this Category
Contract Research Organizations (CROs) are crucial in biomedical R&D. Their data primarily comes from clinical trial protocols, investigator brochures, informed consent forms, case report forms (CRFs), and various trial reports. These documents exist as PDFs, Word files, or scanned images. They have complex structures and contain extensive specialized terminology, dosages, time points, measurement units, and adverse event descriptions. Data updates frequently, especially during clinical trials, where protocol amendments, data entry, and safety reports continuously generate new versions. Fields include drug names, trial phases, subject IDs, metric values, and statistical results. Units involve milligrams, milliliters, days, and percentages, often accompanied by specific medical abbreviations and standards.
Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts
The complex structure and high update frequency of CRO R&D documents impose specific constraints on multiturn conversations and prompts. First, documents contain numerous tables, charts, and nested structures. Traditional text chunking methods struggle to preserve semantic integrity, leading to information loss or poor relevance during Q&A. Second, specialized terminology and abbreviations require prompts to have strong domain knowledge understanding to accurately parse user intent and avoid misunderstandings. High update frequency means the knowledge base must quickly synchronize the latest document versions for the conversation system to provide real-time, accurate information. Additionally, precise extraction and comparison of specific fields and units in multiturn conversations require prompt design to consider numerical types and unit conversions to support engineers in data verification or analysis.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
Chunk size | 500-800 characters | Balances context completeness and recall efficiency. Avoids individual chunks being too long (diluting key information) or too short (losing context). |
Recall count | 8-12 entries | CRO documents have strong interconnections. Increasing recall items helps cover more potentially relevant paragraphs, improving answer accuracy. |
Similarity threshold | 0.75-0.85 | Ensures recalled results are highly relevant to the query, filtering out noise, and improving precision. |
Rerank result count | 3-5 entries | After re-ranking model filtering, retains the most relevant few items, improving final answer quality and response speed. |
maxContext | 4096 tokens | Accommodates long sentences and complex descriptions in CRO documents, ensuring the model has enough context to understand multiturn conversation intent. |
ENABLE_HISTORY_MEMORY | True | Maintains coherence in multiturn conversations, which is crucial for iterative queries in CRO R&D processes. |
Three Common Pitfalls
- Symptom: The model fails to correctly extract specific drug dosages or trial periods in a conversation. Reason: The prompt did not explicitly instruct the model to focus on numerical types and units, or chunking separated critical numerical values from their units.
- Symptom: A user asks for the latest revision of a trial protocol, but the model returns outdated information. Reason: The knowledge base synchronization mechanism did not update the latest document version in time, or index rebuilding was delayed.
- Symptom: After a user inputs "read the code in directory xx," the model only provides a text response and cannot execute subsequent operations. Reason: The FastGPT UI is not integrated with or configured for external executors like Open-Interpreter, preventing the model from converting conversational intent into actual commands.
How to Verify Configuration
- Upload and index multiple core CRO documents containing tables and charts. Verify that chunking results preserve key structural information.
- Conduct multiturn conversation tests for R&D questions of varying complexity, including specialized terminology queries, data comparisons, and process consultations. Evaluate the model's understanding of intent and accuracy of information extraction.
- Simulate document updates by uploading new document versions and performing queries. Check if the model accurately identifies and uses the latest information, confirming the knowledge base update mechanism is effective.
- Design queries containing specific numerical values and units. Check the precision of numerical values and consistency of units in the model's results, ensuring they meet expected thresholds.
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.