Data Characteristics
GMP compliance data originates from regulations, guidelines, inspection reports, and deficiency letters published by drug regulatory agencies worldwide. It also includes internal quality management system documents, batch production records, and validation reports. Data update frequency is relatively stable, typically occurring with regulatory revisions or new guideline releases. Internal records, however, are continuously generated. Document structures are primarily normative texts, such as Good Manufacturing Practice (GMP) regulations, containing numerous clauses, appendices, and interpretive documents. Fields and units are highly specialized. Examples include "Batch No.", "Expiration Date", "Manufacture Date", "Deviation ID", and "Change Control No.". These often involve measurement units (e.g., mg, mL, ℃).
Constraints on Multi-Turn Conversations and Prompts
The normative and specialized nature of GMP compliance data requires multi-turn dialogue systems to maintain high accuracy in understanding user intent, specialized terminology, and context. Regulatory clauses have strong inter-references and associations; the model needs to integrate information across documents and sections. The lower update frequency means core knowledge points remain relatively stable after knowledge base construction. However, the continuous nature of internal records requires the system to dynamically index the latest batch data. The large number of fields and units challenges prompt construction. Prompts must clearly specify retrieval fields and correctly identify and process unit conversions. In multi-turn conversations, users may ask in-depth questions about a specific regulatory clause. The system must maintain conversational coherence and accurately cite original text.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8 turns | GMP compliance questions often require multi-turn tracing. Excessive context introduces noise. 8 turns balance coherence and efficiency. |
Chunk size (Segment Length) | 500–800 characters | GMP clauses and interpretive texts have moderate length. 500–800 characters ensure semantic completeness and prevent splitting critical information. |
Recall count (Recall Count) | 8–12 items | Regulatory provisions are highly interconnected. Increasing the recall count helps cover more related knowledge points, improving answer accuracy. |
Similarity threshold (Similarity Threshold) | 0.78 | GMP terminology is professional and rigorous. A higher similarity threshold ensures retrieved results are highly relevant to user queries, avoiding misinformation. |
Rerank result count (Reranked Return Count) | 5 items | Reranking the recalled items focuses on the 5 most relevant pieces of information, reducing the large model's processing burden and improving precision. |
temperature | 0.1–0.3 | GMP compliance requires rigorous answers. A lower temperature value reduces the model's free generation, ensuring information accuracy. |
Common Mistakes
- Observation: The large model's answer does not cite any knowledge base content, or the cited content has a weak association with the question. Reason: The
Similarity threshold(Similarity Threshold) is set too high, filtering out relevant documents. Alternatively, the knowledge base segmentation strategy is unreasonable, splitting critical information. - Observation: During the conversation, the system fails to correctly identify professional terminology or measurement units, leading to retrieval failures or biased answers. Reason: The prompt does not explicitly specify parsing rules for professional vocabulary, or specific fields are not pre-processed.
- Observation: When a user asks about the quality records of a specific batch product, the system cannot provide the latest or accurate data. Reason: The knowledge base does not timely synchronize internal dynamic production and quality management records, or the indexing mechanism does not effectively handle timestamped data.
Validation of Configuration
- Test the multi-turn dialogue system's ability to accurately cite clause numbers and specific content from Good Manufacturing Practice regulations for typical GMP audit Q&A scenarios.
- Randomly select at least 20 compliance questions containing specialized terminology and measurement units. Verify the accuracy of all cited information in the system's answers and check if measurement units are handled correctly.
- Simulate user queries about recently updated regulations or internal quality records. Verify the system's ability to recall and utilize the latest data for answers, checking the
data_update_timestampfield.
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.