Multi-Turn Conversations and Prompts for Retail Chain

Data Characteristics

Retail chain quality documents include product specifications, SOPs (Standard Operating Procedures), HACCP (Hazard Analysis and Critical Control Point) plans, GSP (Good Supply Practice) or GMP (Good Manufacturing Practice) related files, internal audit reports, supplier qualification certificates, employee training records, and store inspection checklists. Data sources are diverse, ranging from centralized procurement and quality inspection departments to daily operational records generated at the store level. Update frequency varies by document type. Product specifications may update with each batch, SOPs and HACCP plans are typically revised annually, while store inspection checklists and training records are updated daily. Document structures are often structured or semi-structured, such as procedural documents with fixed section titles or tables containing specific fields. Key fields include batch number, expiration date, production date, supplier code, store number, inspection item, result (pass/fail), and corrective actions. Units include weight (grams, kilograms), volume (milliliters, liters), temperature (Celsius), and time and date.

Constraints on Multi-Turn Conversations and Prompts

The characteristics of retail chain quality document data impose several constraints on multi-turn conversation and prompt design. First, diverse and frequently updated document sources require the RAG (Retrieval-Augmented Generation) system to efficiently index and synchronize data in real-time. Store daily operation queries may involve the latest product batch information, and the conversation system must access and understand this dynamic data. Second, the coexistence of structured and semi-structured documents presents challenges for information extraction and knowledge graph construction. For example, step descriptions in SOPs and specific numerical values in inspection checklists require different parsing strategies. When a user asks "the quality inspection result of a certain batch of products at store X," the system must accurately identify the batch number and store number and retrieve them from the corresponding inspection records. Third, quality documents are highly specialized, containing numerous industry terms and abbreviations. Prompt design must incorporate this specialized knowledge to avoid generating ambiguous or inaccurate answers. Multi-turn conversations need to track product, batch, or store information within the context for refined queries or correlated analysis in subsequent turns, such as asking "Are there similar issues with this batch of products in other stores?" Additionally, for queries with high compliance requirements, the conversation system must ensure answers directly quote the original text and provide document sources to meet audit and traceability needs.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	8192 tokens	Balances long document context with conversation history, prevents loss of critical information, and supports complex query tracing.
`Chunk size` (Segment Length)	800–1200 characters	Accommodates paragraph lengths in documents like SOPs and HACCP plans, ensuring semantic completeness.
`Recall count` (Recall Count)	Top 5–8	Covers multiple potentially relevant quality standards or event records, increasing retrieval accuracy.
`Similarity threshold` (Similarity Threshold)	0.75–0.82	Filters out semantically irrelevant results, improving retrieval precision and reducing incorrect answers.
`Rerank result count` (Rerank Return Count)	Top 3	Focuses on the three most relevant pieces of information, reduces model processing burden, and improves response speed.
`LLM_TIMEOUT_SECONDS`	60 seconds	Provides sufficient model inference time for complex queries while avoiding long user waits.

Common Pitfalls

The conversation system returns "no relevant information found" or an empty result when processing queries involving product batch numbers or store codes. This occurs because the document index fails to correctly identify and extract these frequent and critical entity information, leading to retrieval failure.
When a user asks about a specific quality standard or a step in an SOP, the AI's answer is too general or does not match the specific procedure. This may be because the prompt does not sufficiently guide the model to focus on the original document content, leading to over-generalization or inference.
When a user attempts to trace the handling process of a non-conformance event, the conversation breaks or cannot link to subsequent corrective action records. This usually happens because the multi-turn conversation's context management mechanism fails to effectively transfer and utilize key entities from previous queries (e.g., non-conformance report number).

Validation Steps

Design a series of queries with key fields such as batch, store, and date for different types of quality documents (e.g., SOPs, product specifications, inspection reports). Verify that the AI's returned results accurately quote the original text and that field values match.
Conduct multi-turn follow-up tests to check if the system correctly understands and maintains context. For example, first ask "the quality inspection result of a certain product batch," then follow up with "have recall measures been taken for this batch of products?" Confirm the relevance of information between turns.
Examine retrieval logs and model outputs to observe the actual performance of parameters like token usage, Recall count (Recall Count), and Similarity threshold (Similarity Threshold). Confirm they meet expectations for typical queries, without excessive irrelevant recalls or low similarity scores.

Note: 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.