Multi-Turn Conversations and Prompts for Supplier Audit

Data Characteristics in this Category

Supplier audit data in the biopharmaceutical sector typically originates from audit reports, qualification certificates, production batch records, quality management system documents (e.g., GMP, ISO certifications), and contracts. Data update frequencies vary; qualification certificates may update annually, while production records generate in real-time per batch. Document structures are primarily unstructured text, supplemented by tabular data such as equipment calibration records and personnel training files. Fields and units are highly specialized, for example, "USP grade," "EP standard," "batch number," "production date," "expiration date," "inspection batch," and "sterility assurance level (SAL)." These require extremely high precision and traceability for numerical values.

Constraints from these Characteristics on Multi-Turn Conversations and Prompts

The unstructured nature and specialized terminology of supplier audit data require multi-turn conversational systems to have robust text understanding capabilities for accurate key information extraction. Varying data update frequencies make knowledge base synchronization mechanisms critical to ensure conversations rely on the latest valid information. For example, expired qualifications directly impact audit conclusions, requiring the system to identify and flag them. High precision numerical values and traceability requirements restrict the model's "free interpretation" of facts when generating responses; it must strictly cite original text or follow clear logical chains. Additionally, multi-turn conversations may involve in-depth inquiries about specific batches or standards, requiring the system to maintain context and perform complex reasoning.

Configuration Settings

Configuration Item	Recommended Value	Rationale for this Value
`maxContext`	`4096`	Accommodates long descriptions and context dependencies common in audit reports.
`Chunk size` (Segment Length)	`800–1000 characters` (characters)	Balances semantic completeness and retrieval efficiency, avoiding critical information splitting.
`Similarity threshold` (Similarity Threshold)	`0.75–0.8`	Ensures highly relevant recall of audit clauses or qualification information, reducing misjudgments.
`Recall count` (Recall Count)	`Top 5–8 entries` (top 5–8 items)	Covers relevant information potentially needed in multi-turn conversations, balancing precision and breadth.
`Rerank result count` (Reranked Return Count)	`3 entries` (3 items)	Selects the most relevant segments from the recalled set, improving response quality.
`API_TIMEOUT_SECONDS`	`60 seconds` (seconds)	Addresses potential delays from complex queries and large data processing.

Three Common Mistakes

Dialogue shows "no relevant qualification information found" or cites outdated data: This occurs because the knowledge base synchronization mechanism did not trigger effectively, leading the model to make judgments based on old data.
Model misunderstands specialized terms like "USP grade" or "sterility assurance level," generating generic explanations: This happens due to a lack of clear definitions or contextual guidance for domain-specific terminology in the prompt.
The LLM option is switched in the debugging interface, or "character limit exceeded" is prompted: This is caused by the connected deepseek model having a max_tokens parameter set too low, preventing it from processing FastGPT's constructed long prompts or multi-turn conversation history.

How to Confirm Correct Configuration

Simulate various audit scenarios to check if the system accurately identifies and cites the latest supplier qualification documents.
Input audit questions containing specialized terminology to evaluate the system's understanding and explanation of key information like "GMP certification" or "batch number."
Conduct long dialogue tests to confirm the system maintains contextual coherence after multiple turns and can pursue in-depth inquiries about specific audit findings.
Check if the system avoids API_TIMEOUT_SECONDS errors or max_tokens limitations when processing large audit reports or production records.

Note: The values provided are common starting points. Measure them against your own samples to determine optimal settings.

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.