Multi-turn Conversations and Prompts for GMP Compliance

Data Characteristics

GMP compliance documents include Standard Operating Procedures (SOPs), batch production records, testing methods, validation reports, deviation records, and change control records. These documents are typically in PDF, Word, or scanned image formats. They have a rigorous structure and contain extensive specialized terminology, technical parameters, and regulatory citations. Data update frequency is relatively low, with revisions primarily occurring during regulatory updates, process changes, or annual reviews. Document fields cover production batches, dates, operators, equipment numbers, critical process parameters (e.g., temperature, pressure, time), material batch numbers, and test results (e.g., content, purity, dissolution rate). Units are precise to multiple decimal places, with strict requirements for unit accuracy, such as °C, kPa, min, and mg/mL.

Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts

The rigor and specificity of GMP documents require the dialogue system to accurately understand specialized terminology and context, avoiding ambiguity. The low update frequency of documents means that real-time requirements for RAG retrieval are not high, but there is a need for historical version traceability. Multi-turn conversations require accurate identification of user intent. For example, a user might first ask for an SOP version, then inquire about specific operating steps or parameters based on that version. The numerous precise numerical values and units in documents require the model to accurately cite original data in its responses, without arbitrary summarization or generalization. Additionally, there are many regulatory citations and cross-references. Prompt design needs to guide the model to identify and link logical relationships between different documents, ensuring the accuracy of compliance judgments.

Configuration Settings

Configuration Item	Suggested Value	Rationale for Value
`maxContext`	`2048`	GMP documents often have long paragraphs containing complex technical details, requiring a larger context window for coherence.
`Recall Count`	`8–12`	Ensures coverage of multiple relevant document segments, increasing the comprehensiveness of recall, especially when questions involve multiple linked documents.
`Similarity Threshold`	`0.78`	A high threshold helps filter out irrelevant recall results, improving answer precision and reducing the risk of hallucinations.
`Rerank Return Count`	`5`	Further refines the most relevant segments based on high similarity recall, optimizing the quality of the final answer.
`Segment Length`	`500 characters`	Retains sufficient information to understand the complete context of GMP operating steps and technical parameters, reducing semantic fragmentation.
`Segment Overlap`	`50 characters`	Appropriate overlap helps the model understand complete sentences and semantic connections spanning segments, preventing information loss.

Three Common Mistakes

"No relevant information found" or "Unable to answer" in a conversation: maxContext or Recall Count is insufficiently configured in multi-turn conversations, preventing the model from obtaining enough context to understand user intent or link multiple document contents.
Model response cites incorrect batches or parameters: Similarity Threshold is set too low, leading to the recall of document segments not entirely relevant to the user's question, and the model generates answers based on incorrect information.
After multiple questions, the system still cannot maintain conversational coherence: The Session Retention Time parameter is not correctly set, causing the system to fail to retain enough historical conversation information after multiple user interactions, preventing effective multi-turn dialogue.

How to Confirm Proper Configuration

Test whether multi-turn conversations can accurately track context and provide correct answers for typical GMP compliance questions, such as first asking for an SOP version and then asking for specific operating steps.
Randomly select key numerical values and specialized terms from model responses and verify if this information exactly matches the data in the source documents, including units and precision.
Simulate questions from different user roles (e.g., production personnel, quality control personnel) and check if the system can specifically recall information from different documents and verify if the answers meet the compliance requirements for the respective roles.

Note: The values provided are common starting points. They 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.