What the documents look like
Livestream after-sales work clusters after a single broadcast, and the commitments a host made on camera shape most of the incoming requests. For store service and internal training, the working materials are customer-service IM records, refund applications, reshipment orders, and script templates, refreshed after each broadcast and daily. Two input streams matter at the multi-turn prompt step. One is a customer pressing the same order repeatedly about refund or reshipment status. The other is a trainee practicing a consistent explanation of what the host promised. The daily rhythm affects when a review is scheduled; it does not by itself justify batch synchronization or transaction-triggered processing.
Constraints at the multi-turn prompts step
Multi-turn dialogue means the system carries earlier turns forward up to the memory-turn setting. In a training setting a trainee may ask about a refund and then about a reshipment inside one session, so the prompt needs to state the material scope, the answering task, and how to handle missing evidence. History review distinguishes a new session from a continued session by comparing what the input actually carries. For question optimization, knowledge-base continuous dialogue requires checking reference resolution and question expansion; whether short questions qualify is judged from the actual material, not assumed. Model and length checks cover the context the model supports and the reply settings, tested against long documents combined with follow-up questions. Reply format follows the downstream consumer; selecting json_schema requires filling the corresponding format requirement and verifying the returned structure. Retrieval finds material and does not constitute authorization. Forms collect values, and business verification, approval, database writes, and external synchronization each need their own downstream implementation.
Hypothetical drill sample: the source text reads "The refund application for order A from this broadcast was submitted on March 1 and its status is pending review; the reshipment order for the same order was created on March 2 and its status is pending warehouse processing. The host stated verbally during the broadcast that this order could be reshipped with priority." The draft sentence under review reads "The refund for order A has been approved and the reshipment has been executed." The subject needed for judgment is order A, the objects are the refund application and the reshipment order, the statuses are pending review and pending warehouse processing, and the valid range is March 1 to March 2. The draft writes pending review as approved and pending warehouse processing as executed, which contradicts the mutually exclusive values of the same status fields in the source.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting value | Reason for the choice and comparison method |
|---|---|---|
| Memory turns | Hypothetical starting value of 2 turns | Store-training follow-ups are mostly two adjacent turns; compare against the default and adjust as needed |
| Reply limit | Hypothetical starting value of a short reply | A consistent explanation of commitments needs to be concise; compare against a longer proposal-style reply |
| Temperature | Hypothetical starting value of low | Customer service and knowledge-base Q&A need stability; compare against a divergent value |
| Reply format | Hypothetical starting value of keeping the default | No downstream consumer needs a fixed structure; compare against a json_schema option |
| Hide AI output | Hypothetical starting value of off | Training needs to observe intermediate results; compare against on, where only the final reply is visible |
| Thinking configuration | Hypothetical starting value of keeping the default | Simple after-sales questions do not need high thinking intensity; compare against a higher intensity |
Three common errors
- Writing a pending-review refund application as approved. The input is the status field in the sample source text, the comparison object is "approved" in the draft sentence, and pending review and approved are mutually exclusive values of the same field, so their co-occurrence is a contradiction.
- Writing a pending-warehouse-processing reshipment order as executed. The input is the reshipment status in the sample source text, the comparison object is "executed" in the draft sentence, and the two are mutually exclusive in the same field, so their co-occurrence is a contradiction.
- Treating a retrieval result or a form submission as authorized execution. The input is the retrieval return or the form value, the comparison object is whether a business action occurred, and retrieval and form collection do not constitute authorization, so the business action needs a downstream implementation.
How to verify the setup
- Input the sample source text and the draft sentence, then compare whether "approved" appears in the draft. Its appearance is judged a contradiction with the source status; its absence is recorded as pending verification.
- Input the sample source text and the draft sentence, then compare whether "executed" appears in the draft. Its appearance is judged a contradiction with the source status; its absence is recorded as pending verification.
- Input a retrieval request and compare the returned results against the material scope, observing whether relevant records are hit. For empty-result handling, a clearly labelled paper input of [] can be used to check the downstream design; a fictional rule-based query is not claimed to return []. Libraries built on different embedding models cannot be associated together, so check the identifier first and exclude non-matching ones, recording what is actually observed.