What the documents look like
For copy drafting in Amazon FBA preparation, this page examines FastGPT multi-turn prompts. Trace material and audience evidence so an older draft does not become a default source of fact. The examples are hypothetical.
Constraints at the multi-turn prompts step
The proposed inbound plan names wireless earbuds and states noise cancellation as a function. Those are the supplied facts for this draft; the attachment gives no cancellation magnitude, battery performance or audience evidence. The prompt should distinguish wording available for restatement from performance claims needing support. Compare the same writing request in a fresh conversation and with history, checking for features carried over from another product. Trace the draft’s functional statements back to the current attachment.
Configuration and implementation checks
| Check | Evidence for this copy | Deviation to inspect |
|---|---|---|
| Functional wording | Earbuds and noise cancellation are explicit | Unsupported magnitude or battery claims |
| History input | Current scope is the earbuds source | Features carried from another product |
| Material difference | Inbound text says plastic; label says metal | A material adopted merely because it appeared earlier |
| Question rewrite | Retain the product and both sources | A source dropped during reference resolution |
| Audience | The smartwatch audience source is absent | Age or occupation inferred from the name |
Documented conditions
| Documented item / implementation check | Condition |
|---|---|
Prompt and task | Define sources, the task and evidence-gap handling. |
Conversation history | Inspect the history actually submitted. |
Question optimization | Review reference resolution and question expansion. |
Model and length | Inspect context and reply length separately. |
Reply format | json_schema needs its format and parsing validation. |
Three common errors
- If historical plastic overwrites metal on the label, compare actual history, current attachment and draft to locate source loss. More conversation cannot adjudicate the material.
- If supplied noise-reduction wording becomes a degree, battery duration or definite audience, trace each condition. A function name does not establish those details; retain gaps.
- If a watch without positioning borrows another item’s age or occupation, retain product identities and audience claims. Inspect historical carryover or generated additions, then retest with applicable positioning evidence.
How to verify the setup
The smartwatch lacks a target-user description, so inspect each audience statement in the draft. The product name supplies no age, occupation or usage-scenario evidence, and an audience attributed to another product in history cannot simply be transferred. If positioning material is later provided, record whether it enters the actual input before repeating the same writing task. Fluent wording or an acceptable response format is a separate result; the audience description still needs support in the source.
| Material context | Scope |
|---|---|
| Document types | FBA Inbound Plan, Carton Label, Inventory Report and Compensation Application |
| Candidate sources | Amazon Seller Central, FBA Inventory Report, Freight Forwarder Tracking and ERP Purchase Order |
| Technical baseline | FastGPT v4.17.2 |