# Stop overwriting your data when comparing inventory reports if

> Source: <https://promptcube3.com/en/threads/5930/>
> Published: 2026-08-11 16:45:28+00:00

# Stop overwriting your data when comparing inventory reports if

## The logic of a robust AI workflow for reconciliation

When using an LLM agent to handle these tasks, the sequence of operations is everything. You cannot jump straight to comparing quantities.

1. **Identifier Mapping:** First, the AI must match rows based on a stable ID (like a SKU or Serial Number). If the ID doesn't match, the row is an automatic exception.

2. **Value Comparison:** Only after the IDs are locked does the AI compare the actual stock levels or statuses.

3. **Registry Creation:** Instead of a "Corrected" column, the AI generates a report showing: ID, Value A, Value B, Difference Type, and a Manual Review flag.

## Implementing this with prompt engineering

To get an LLM to handle this without "hallucinating" a fix or simplifying the data too much, you need to constrain it to act as a data auditor rather than a data cleaner. Here is a prompt I've been using to turn raw CSV exports into a professional discrepancy registry.

```
Act as a Data Reconciliation Expert. I will provide you with two datasets (Dataset A and Dataset B). 

Your goal is to identify discrepancies without deleting the original source data. Follow these strict logic steps:

1. Match rows exclusively by the [Unique ID] column.
2. If a [Unique ID] exists in A but not in B (or vice versa), mark it as "Missing Record" and list it in the exceptions.
3. If the [Unique ID] matches but the [Quantity/Status] differs, do NOT pick a winner. Instead, create a registry entry with the following format:
   - ID: [The Unique ID]
   - Value_A: [Exact value from Dataset A]
   - Value_B: [Exact value from Dataset B]
   - Diff_Type: [e.g., Quantity Mismatch]
   - Review_Status: "Pending Manual Check"

4. Output the results as a list of discrepancies. Do not summarize; provide every single row that fails the match.

Dataset A:
{{Dataset_A}}

Dataset B:
{{Dataset_B}}
```

## Why this approach beats a simple VLOOKUP

A standard spreadsheet formula tells you *that* something is different, but an LLM agent can help categorize *why* it might be different based on surrounding context (like timestamps or location codes).

The most critical part of this deep dive is the "Review Status." By forcing the AI to label a row as "Pending Manual Check," you create a clear boundary between automated detection and human decision-making. If you just create a "Final Value" column, you're gambling that the AI (or the formula) chose the correct source. In high-stakes inventory management, that's a risk you can't afford.

Keep the raw values, track the difference, and only close the loop after a human eyes the physical stock.

[Next Stop overpaying for ChatGPT Pro if you're just learning to code →](/en/threads/5803/)

[these real-world AI monetization case studies](https://tanyan888.com/), with plenty of directly applicable cases.