Data Characteristics
CDMO (Contract Development and Manufacturing Organization) quality documents include batch production records, inspection reports, deviation management, change control, validation documents, and audit reports. These documents are typically in PDF, Word, or scanned image formats, with varying degrees of structure. Data sources are extensive, covering R&D, manufacturing, and quality control. Update frequency varies by document type; batch production records are generated per batch, while validation documents might update every few months or years. Documents contain numerous specialized terms, abbreviations, batch numbers, dates, equipment IDs, and other critical fields. Units include concentration (e.g., mg/mL), temperature (e.g., ℃), and time (e.g., min). Specific naming conventions and version control requirements are common.
Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts
The complexity of CDMO quality documents imposes specific requirements on multi-turn conversation and prompt construction. Low document structure and diverse formats necessitate robust document parsing capabilities to ensure accurate information extraction and vectorization. The coexistence of multiple document versions requires the conversation system to identify and reference specific version information based on context, preventing confusion. The prevalence of specialized terms and abbreviations means prompt design must consider glossaries and synonym mapping to improve semantic understanding accuracy. Varying update frequencies require the vector database to support incremental updates and quickly integrate new data, ensuring real-time accuracy of conversation content. Queries often involve cross-comparison of multiple batches and different parameters, so prompts must guide users to clearly express their query intent for precise results.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 | Balances multi-turn conversation coherence with computational resource consumption, preventing model performance degradation due to excessively long contexts. |
Chunk size (Segment Length) | 500–800 characters (characters) | Accommodates potentially long descriptive paragraphs in quality documents, ensuring semantic completeness and minimizing splitting loss. |
Recall count (Recall Count) | Top 5 entries (top 5) | Considers retrieval efficiency and relevance, reducing interference from irrelevant information and focusing on the most relevant knowledge snippets. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures the precision of recalled content. CDMO documents demand high accuracy; a lower threshold might introduce noise. |
Rerank result count (Rerank Return Count) | 3 | Further refines the most relevant content from the recall results, improving the quality of information presented to the user. |
prompt | Specify fields like batch, product, date | Guides the model to focus on key information specific to CDMO documents, enhancing the accuracy and professionalism of answers. |
Common Pitfalls
- Symptom: AI responses mix data from different batches or cite outdated document versions. Reason: The vector database does not effectively handle document version control, or document lifecycle states are not correctly marked during data updates.
- Symptom: When users query specialized terms, the AI cannot understand or provides irrelevant answers. Reason: The prompt or knowledge base does not sufficiently include a glossary of CDMO-specific specialized terms and abbreviation mappings.
- Symptom: The API call's conversation history is empty, preventing tracking of user interactions. Reason: The
conversationIdparameter is not correctly passed during API calls, causing each dialogue to be treated as a new session.
Verification Steps
- Perform simulated queries covering quality parameters for different batches, products, and time points. Verify that AI-provided data matches original documents and confirm version correctness.
- Ask questions using CDMO-specific specialized terms and abbreviations. Observe if the AI accurately understands and provides professional, unambiguous answers.
- Send continuous multi-turn dialogue requests via API. Check if the
conversationIdparameter is correctly passed between rounds and verify that the dialogue history is completely saved in the backend.
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.