Multi-turn Conversation and Prompts for Supplier Audit

Data Characteristics

Supplier audit regulation data primarily originates from internal quality management system documents, audit standards, inspection records, rectification reports, and external regulatory documents. These documents are often in PDF, Word, or scanned image formats, with varying degrees of structure. Update frequency is typically low, occurring mainly during regulatory revisions or internal process adjustments. Document content covers audit scope, scoring criteria, non-conformity classification, and rectification timelines. Fields include audit number, supplier name, audit date, auditor, clause number, identified issues, corrective actions, and completion status. Some documents may contain charts and attachments.

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

The low update frequency of supplier audit regulation documents means that once the knowledge base is built, content stability is high. However, initial data cleaning and annotation require significant effort. Document structure is complex, containing numerous specialized terms and cross-references. This demands strong semantic understanding from the model to accurately identify logical relationships between regulations. In multi-turn conversations, users might trace specific clauses across different audit reports, requiring the system to effectively link knowledge points. Diverse fields, including specialized units (e.g., "batch number," "deviation level"), necessitate prompt design that guides the model to precisely extract and interpret this information, preventing parameter parsing failures like "Unexpected end of JSON input" or "return contains newline characters."

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Balances the completeness of regulatory clauses with model processing efficiency, avoiding information overload from overly long contexts.
`Recall count` (Recall Count)	`Top 8–12 entries`	Supplier audit clauses are highly interconnected. Increasing the recall count enhances context continuity in multi-turn conversations.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Ensures recalled knowledge segments are highly semantically relevant to audit regulations, filtering out non-core information.
`Rerank result count` (Reranked Recall Count)	`Top 5 entries`	Further refines the most relevant regulatory clauses to the user's query, improving answer accuracy.
`maxContext`	`4096 tokens`	Accommodates the complexity and specialized nature of audit regulation text, providing sufficient context for the large language model's reasoning.
`promptTemplate`	Calibrate based on actual testing	Must include instructions such as: "Based on the provided supplier audit regulations, answer the user's question about clause X, and cite the specific clause number."

Common Pitfalls

Conversation returns Unexpected end of JSON input or JSON format errors: This usually occurs when the model generates text containing special characters or non-standard JSON structures, causing downstream parsers to fail.
Errors when uploading large audit documents: File size or processing time exceeds the system's configured UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS limits.
Model forgets early questions in multi-turn conversations: maxContext is set too small, leading to truncation of historical conversation information and inability to maintain conversation context.

How to Confirm Proper Configuration

Ask questions about audit clauses of varying complexity. Check if the model accurately cites corresponding clause numbers and content.
Simulate multi-turn follow-up questions from a user about a specific audit finding. Verify that the model maintains conversation coherence after multiple interactions.
Upload an audit report containing various formats (e.g., tables, images). Confirm that the file parses correctly and extracts text content.
Test during peak hours or simulate high concurrency scenarios. Observe system response speed and error logs to ensure no parsing errors like Unexpected end of JSON input occur.

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.