Data Characteristics
GMP compliance data originates from pharmaceutical companies' internal quality management system documents, SOPs (Standard Operating Procedures), batch production records, inspection reports, deviation handling records, change control documents, and audit reports. These documents are often in PDF, Word, or scanned image formats, with low structural consistency. Data update frequency depends on regulatory changes, internal process optimizations, and production batches. Updates typically occur quarterly or annually, but deviation and change records may be generated in real-time. Documents contain extensive technical terms, abbreviations, and specific table formats. Field content includes production process parameters, quality control indicators, equipment calibration records, and personnel training files. Units cover various measurements such as temperature (℃), pressure (Pa), time (min/h), and concentration (mg/L).
Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts
The unstructured and specialized nature of GMP compliance documents requires accurate context understanding in multi-turn conversations to avoid misinterpretations due to ambiguity. The uncertain data update frequency necessitates an efficient incremental update and version management capability for the knowledge base, ensuring conversations are based on the latest compliance requirements. The large number of technical terms and abbreviations demands more sophisticated prompt construction, requiring vocabulary lists or domain ontologies to enhance understanding. Additionally, specific table formats and multi-unit data within documents require the conversation system to identify and correctly parse information during extraction, such as distinguishing inspection results from different batches and performing unit conversions or data aggregation based on user queries. The ability to trace historical records is also essential for the conversation system when handling deviation or change inquiries.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size | 500 characters | Balances semantic integrity and recall efficiency, preventing information overload in a single segment. |
Recall count | Top 8 entries | Covers a broader context, improving hit rates for complex queries. |
Similarity threshold | 0.75 | Filters out low-relevance content, ensuring precision of recalled information. |
Rerank result count | Top 3 entries | Reduces LLM input length and cost while maintaining accuracy. |
maxContext | 3200 tokens | Accommodates context requirements for long documents and multi-turn conversations, ensuring completeness. |
promptTemplate | Includes keywords like "GMP", "SOP", "batches Batches" (batch), "Compliance" (compliance), and explicitly requests source citation. | Guides the model to focus on the compliance domain, enhancing professionalism and traceability of answers. |
Common Mistakes
- The conversation returns "no relevant information found" or provides overly generic answers. This occurs due to an improper knowledge base segmentation strategy, leading to truncated key information or insufficient context for effective responses.
- The AI platform returns a 400 status code. This may relate to syntax errors in the prompt template or the use of unsupported instructions for the model, causing request parsing to fail.
- Answers cite outdated or non-latest versions of compliance documents. This happens because the knowledge base is not updated promptly or lacks an effective version management mechanism.
How to Verify Configuration
- Conduct multi-turn conversation tests with different types of GMP documents (SOPs, batch records, deviation reports) to check the accuracy and completeness of responses.
- Simulate user queries for specific batch production parameters or quality control indicators. Cross-reference the data in AI responses with original documents and verify unit correctness.
- Test the system's response to recently updated or changed compliance requirements. Observe if the AI provides correct guidance based on the latest knowledge and cites updated documents.
The values provided are common starting points and should be measured against the reader's 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.