Use Case: Model Validation (Custom Column Only)

Filter false positives out of your search results (build V3.5.1.0)

What This Does.

A single AI column that answers one question per listing — is this actually the model I’m searching for? — so keyword noise stops burying the real results. No detailed analysis, no elaborate display: one Yes/No column, built for speed.

Table of Contents #

1. The Problem
2. When to Use This
3. What You’ll Build
4. Setup Steps
5. The Prompt
6. Suggested Workflow
7. Adapting for Your Products
8. Troubleshooting

The Problem #

Search for a specific model number and eBay gives you every listing where that number appears — for any reason. Say you buy Intel i7-12700 processors:

• “16GB DDR4 RAM for 12700K systems” → it’s RAM, not a CPU
• “Motherboard bundle — supports i7-12700 (CPU not included)” → no CPU in the listing
• “Lot of 12 mixed processors” → 12 is a quantity
• “i5-12400 with 12-month warranty” → wrong chip, 12 is the warranty

Your keyword matched. The product didn’t. At high search volume, this noise costs real time.

When to Use This #

• You search for a specific model range with variants (i7-12700, 12700K, 12700KF).
• High search volume creates many false positives to wade through.
• You want fast filtering, not a full analysis readout.
• The determination is simple: is it or isn’t it your target model?

Same pattern works for GPUs (“RTX 4090” vs “lot of 4”), camera models, part numbers — any product where the model number also shows up as a quantity, spec, or accessory reference.

What You’ll Build #

Keep the Display Template Minimal.

The value here is the column, not the panel. The profile still wants a display template — ask your assistant for a one-line template alongside the prompt and paste that in.

Setup Steps #

This assumes you have SKU Manager configured — if not, do the Quick Start Guide first.

1. Build the Profile #

1. Open Data → AI Sku Analysis → AI Settings and create or select a profile for this search.
2. AI Configuration: Provider OpenAI (ChatGPT), Model gpt-5.4-mini, your API key, and the validation prompt (below) as the System Prompt.
3. Fields & Columns: tick the fields the prompt relies on — Title, Model, and any item specifics you use (Model Number, Storage, etc.). Under User-Defined Custom Columns (Results), tick Model Match — it’s auto-detected from the prompt. Ignore any junk keys the detector lists.
4. Display Template: paste the minimal template.
5. Save.

2. Create the Filter #

1. Go to Home → Filters → Add.
2. Condition: match your search — e.g. Alias contains your search’s alias, or a simple Title Is not blank catch-all.
3. Action: Apply AI Prompt → Profile: the one you just built.
4. Set a Filter alias, click OK, and confirm the filter is enabled.

The Prompt #

A worked example for the i7-12700 family — adapt the model names and patterns to your product:

# Model Validation Prompt v1.0 — column-only output

## AI COLUMN CONFIGURATION
### "Model Match" — AI Column
Must return EXACTLY one of: Yes, No

| Value | When to use |
|-------|-------------|
| Yes | Listing is genuinely an i7-12700, i7-12700K, or i7-12700KF processor |
| No  | The search term appears for another reason (false positive) |

## ROLE
You validate whether an eBay listing is genuinely one of the target
models, or whether the model number appears for another reason
(quantity, RAM spec, motherboard compatibility, warranty months).

## VALID MODELS
- Intel Core i7-12700
- Intel Core i7-12700K
- Intel Core i7-12700KF

## DETECTION LOGIC
Priority order:
1. Model field (highest) — if populated, trust it first
2. Title — check for true model vs false-positive patterns
3. Description — cross-reference if unclear

False-positive patterns (→ No):
- RAM or motherboard listings that mention the CPU they support
- "CPU not included" anywhere in the listing
- Numbers that are quantities, warranty lengths, or speeds
- A different processor where the number appears incidentally

Model precision:
- 12700 ≠ 12700K ≠ 12700KF — check the suffix explicitly.

## OUTPUT FORMAT
```json
{ "Model Match": "Yes" }
```
Single field, single determination. Before responding, verify the
number refers to the processor model itself and the listing actually
contains the processor.

Suggested Workflow #

Days 1–2: Validation Mode — Hide Nothing Yet #

1. Run your searches normally and watch the Model Match column populate.
2. Review the No rows — are they really false positives?
3. Review the Yes rows — are they really your model?
4. On any wrong call, use the Feedback button and take the trace to your AI project for a targeted fix — see Improving with Feedback.

When You Trust It: Work the Column #

Sort or filter the grid on Model Match so the Yes rows lead and the No rows drop out of your way. Audit the No pile occasionally — a systematic mistake there is a prompt fix, and you want to catch it early.

Adapting for Your Products #

1. Define your valid models — exactly which variants count as Yes.
2. List your false-positive patterns — how does your search term show up when it isn’t your product?
3. Swap them into the prompt — keep the structure, replace the specifics.
4. Test and refine with feedback traces.

Troubleshooting #

• Column not populating: is Model Match ticked under User-Defined Custom Columns? Is the filter enabled, with Apply AI Prompt and your profile? Is the AI server running? Values arrive on fresh results only.
• Column missing from the list entirely: the prompt’s ## AI COLUMN CONFIGURATION entry isn’t clear — exact name, exact values — then reopen the profile so it re-reads the prompt.
• Wrong determinations: capture them with the Feedback button and bring the trace to your AI project. Usually it’s one missing false-positive pattern.

Result: clean search results, with keyword spam sorted out by a column instead of by your eyes.

See also: Setting Up AI Columns · Improving with Feedback · Quick Start Guide