Data Characteristics
Cleanroom management documents include regulatory standards (e.g., GMP Annexes), internal SOPs (Standard Operating Procedures), design qualification (DQ, IQ, OQ, PQ) reports, daily monitoring records, deviation handling reports, and change control documents. Data sources are diverse, ranging from public documents issued by agencies like the National Medical Products Administration to internal corporate documents. Update frequency is relatively stable; regulatory standards typically update every few years, while SOPs and validation reports revise based on production needs and change control processes. Document structures for regulations and SOPs often feature clear chapter divisions and numbering. Validation reports contain detailed test plans, results, and conclusions. Fields and units are critical: common environmental parameters include temperature (℃), humidity (% RH), differential pressure (Pa), and airborne particulate count (particles/m³), alongside microbiological indicators like colony-forming units (CFU/m³). Precision and dimensional consistency are strictly required.
Constraints Imposed by Data Characteristics on "Multi-Turn Conversations and Prompts"
The structured nature of regulatory standards and SOPs allows for information extraction using fixed formats, ensuring accuracy of key fields like chapter numbers and clause content. Low document update frequency means less frequent knowledge base index rebuilding or incremental updates, reducing maintenance costs. Validation reports contain extensive experimental data and detailed descriptions, requiring models to understand complex causal relationships and data correlations. In multi-turn conversations, users may ask in-depth questions about historical trends of environmental parameters, differential pressure requirements for different cleanroom zones, or specific deviation handling processes. Prompt design must consider these structured data characteristics. For example, for differential pressure data, prompts should guide the model to focus on specific values, measurement points, and time ranges. The strictness of fields and units requires the model to accurately cite or convert units in responses, avoiding vague statements. For instance, when a user asks about "cleanroom temperature requirements," the model should differentiate temperature ranges for various cleanroom classifications and provide numerical values with units.
Configuration Strategy
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Cleanroom document paragraphs are often long, containing multiple key information points; too short fragments context, too long adds irrelevant noise. |
Chunk overlap | 50 characters | Ensures semantic continuity at paragraph boundaries, preventing critical information from being split across different segments. |
Recall count | Top 5–8 entries | Cleanroom management questions often involve multiple related regulations or SOP clauses; recalling multiple entries provides more comprehensive background information. |
Similarity threshold | 0.78–0.85 | Ensures precision of recalled content, filtering out paragraphs with low semantic relevance to the query, reducing misinformation. |
Rerank result count | Top 3 entries | Further refines recalled content, prioritizing core information most relevant to the user's intent, improving response efficiency. |
System Preset Prompt | Calibrate by actual measurement | Must include instructions such as "As a cleanroom management expert, strictly answer questions based on provided documents and cite sources." |
Three Common Pitfalls
- Phenomenon: In multi-turn conversations, the model cannot accurately distinguish parameter requirements for different cleanroom classifications. Reason: Cleanroom classification information was not sufficiently extracted or tagged during original document parsing, leading to a lack of differentiation in the knowledge base data.
- Phenomenon: When a user asks about a deviation handling process, the model provides only a general answer and fails to cite specific SOP numbers or change records. Reason: Prompt design was too broad, failing to explicitly require the model to cite specific identifiers from the documents in its response.
- Phenomenon: When continuously inquiring about historical data for an environmental parameter, the model forgets the time range or specific monitoring point mentioned in previous turns. Reason: The
maxContextparameter was set too small, leading to conversation history truncation and the model's inability to maintain long-range context.
How to Confirm Proper Configuration
- Select multiple representative questions, including regulatory queries, SOP processes, and validation data analysis. Test if the model can accurately cite original document text and provide numerical values with units.
- For scenarios involving multi-turn follow-up questions, such as "What is the temperature in cleanroom X? What about cleanroom Y?", check if the model can maintain context and answer correctly.
- Evaluate the model's understanding and accurate use of cleanroom-specific terminology in its responses, such as "laminar flow," "unidirectional flow," and "HEPA filter."
Note: The values provided are common starting points and should be measured 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.