What the documents look like
Short-video affiliate sales rely on long-term content exposure and product-link conversion, without live explanation. Content operators, creator business-development staff and product distribution specialists work daily with short-video scripts, attached-product lists, commission plans and violation feedback. Sources include selected alliance data, short-video playback data, product transaction data and platform rules. These materials update daily and weekly, and copy drafting requires the script, the product list and the commission plan to be compared within one conversation.
The constraints of the multi-turn prompts step come from the materials themselves. Whether a promotional sentence in the script is supported by the attached-product list and commission plan must be checked turn by turn inside the same session. The context carried in by history determines whether the model sees a new session or a continued session. The reply format follows the downstream handling method; choosing json_schema requires filling in the corresponding format requirement and checking the returned structure.
Constraints at the multi-turn prompts step
The prompt states the material scope, the answering task and the handling method when evidence is missing, so the model does not supply business facts on its own. History must be checked against the context actually carried in, distinguishing new-session input from continued-session input. Where a knowledge base needs continuous dialogue, check reference resolution and question expansion; short questions are not assumed to be universally applicable. Model and length require checking the model's supported context and reply settings, testing long documents combined with follow-up questions. Reply format follows the downstream handling method.
Hypothetical paper exercise sample: the source material reads, "The attached-product list for this batch registers the subject as a content studio, the objects as three home-storage products, the status as pending listing, and the effective scope as 1 June 2025 to 30 June 2025. The commission plan states that two of the products carry a commission rate of fifteen percent, and one product is not listed. The first draft of the short-video script contains the sentence 'this storage box bears fifty kilograms.'" The draft sentence to be judged is: "All three products in this batch settle at fifteen percent commission, and the storage box bears fifty kilograms." The comparison object is the commission plan and the product list; the comparison points are whether all three products state a commission rate and whether the load-bearing value has a material source; the expectation is that unlisted items and unsourced figures are marked pending verification.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point for this page | Reason and comparison method |
|---|---|---|
| Memory turns | Retain a small number of history turns | Customer service and knowledge-base Q&A applications usually retain a small number of history turns; compare against the number of context turns carried into a continued session |
| Reply limit | Set by script paragraph length | After enabling, a slider limits reply length; compare against the character count of a complete script paragraph and observe whether truncation occurs early |
| Temperature | Relatively low | Copy and rule-checking scenarios need stable output; compare wording consistency across repeated returns for the same prompt |
| Reply format | Default | Ordinary copy drafting keeps the default; change to json_schema only when a downstream flow reads a fixed structure, and fill in the format requirement |
| Multimodal recognition | Not enabled for now | Enable as needed when the model supports images; compare against recognition results for uploaded script screenshots or product images |
| Thinking configuration | Default | Use the model default configuration; compare against error conditions after switching levels |
These values are paper trial starting points, not official defaults. The documents for memory turns, reply limit, temperature and Top_p give directional guidance only and do not give fixed numbers.
Three common errors
- Writing an unlisted commission rate as a confirmed settlement rate. The input is the commission plan text above; the comparison is whether all three products show a rate field; the observable result is that the draft sentence turns an unlisted item into a confirmed value.
- Treating a load-bearing figure in the script as a verified fact. The input is the script draft and the product list; the comparison is whether the list states a load-bearing value; the observable result is that the draft sentence cites that figure directly.
- Treating similarity or score as a permission or entity filter. The input is the score field returned by retrieval; the comparison is record ownership against the authorized scope; the observable result is that unrelated records still appear in the quote list. Retrieval does not perform authorization judgment.
How to verify the setup
- Input the material text above and the draft sentence; compare whether the commission rate in the draft sentence corresponds item by item with the commission plan; the expectation is that unlisted items are marked pending verification, and writing them directly as confirmed values does not satisfy the condition.
- Input the same material and the load-bearing sentence; compare whether the product list states a load-bearing value; the expectation is that figures without a source are marked pending verification.
- Input a follow-up question containing a reference; compare the number of context turns actually carried into the history; the expectation is that the reference is resolved correctly; if the number of turns carried in is zero, record that observation without inferring a cause.
Empty-result handling can use the explicit paper input [] to check the downstream design, without claiming that a fictional rule retrieval necessarily returns an empty array. Libraries across different vector models cannot be associated together; check the identifier first, exclude non-conforming ones, and record the actual observation.