# Reconciliation Prompt Pack The directions an AI assistant actually needs to help you reconcile two files — copy-paste, in order, tool-agnostic. Works with any capable assistant that can write and run code (a code interpreter / data-analysis tool). The discipline is in what you ask, not which model answers. ## The one rule The model may parse the files, propose the join key, write the matching code, run it, and explain the result. It must **never** be the thing that decides whether two numbers are equal. Code does the matching and the math; the model does the language and the judgment around it. Confirm the output between each step before you paste the next prompt. Replace `system_a.csv`, `system_b.csv`, and `order_id` with your real filenames and key. --- ## Prompt 0 — standing instructions (paste once, at the top) ``` You are helping me reconcile two data files. Follow these rules for the whole session: - Never state a total, count, or difference unless it was printed by code you just ran. If you didn't run code for a number, don't give the number. - Read all ID/key columns as text. Never let an ID become a float. - Do the matching and arithmetic in code (Python/pandas). Use prose only to explain what the code printed. - When unsure, stop and ask. Do not guess a key, a mapping, or a number. ``` ## Prompt 1 — census the files before matching anything ``` Here are two files: system_a.csv and system_b.csv. Do NOT reconcile them yet. First, for each file, run code that prints: - the column names - the row count - 3 sample rows Then list, side by side, which column in A looks like it maps to which column in B. Flag any format differences (currency units, date formats, casing). Do not compute differences yet. ``` ## Prompt 2 — propose the join key (and justify it) ``` Propose the minimal set of columns that uniquely identifies a row in each file. Explain why a single column is or isn't enough. If no single column is unique, propose a composite key. Then run code that counts distinct keys vs. total rows in each file and prints whether the proposed key is actually unique. Reason from the column meanings; do not assume. ``` ## Prompt 3 — write the merge as code I can read ``` Write Python (pandas) that: - reads both files with every ID column as text (dtype=str) - does an OUTER merge on , with indicator=True and validate="one_to_one" - prints the count of left_only, right_only, and both Show me the code first. Don't run it until I say go. ``` ## Prompt 4 — show only the real differences ``` Go. After the merge, for rows present in BOTH files, compute the difference in the column in code. Print ONLY the rows where the difference is nonzero, sorted largest-first. Do not summarize the numbers in prose. Also print the full left_only and right_only rows — those are the missing records, not noise. ``` ## Prompt 5 — classify the differences (model earns its keep) ``` Here is the list of nonzero differences you just printed. Do NOT change any numbers. For each row, classify the likely cause as one of: timing, fee/adjustment, value error, or missing record. Give a one-line reason per row based on the columns available. Flag any you can't classify with confidence. ``` ## Prompt 6 — explain one exception to a human ``` Take row . Context: . In two sentences, explain the most likely cause of the difference for a finance reviewer. If the numbers don't support your explanation, say so instead of forcing one. ``` ## Prompt 7 — verification you run yourself ``` Print three things so I can check your work: - the row census again (left_only + right_only + both = total) - the sum of in each original file, before the merge - 3 specific rows traced end to end, including one that should NOT match ``` Compare the two file totals to a number you already trust (a gross sales figure, a known deposit, last month's balance). If the totals don't tie, stop — the merge dropped or doubled something. --- ## What the pack does NOT do It doesn't make the model trustworthy at arithmetic — it routes every number through code so you never have to trust it. It doesn't replace a repeatable system once the same reconciliation runs every week. And it can't reconcile data you don't have: if a platform nets fees or holds a reserve, you still need the settlement report. Backed by real operator threads — r/FPandA ("AI solutions require very clear directions") and r/BusinessIntelligence (agentic accuracy drifts without maintained instructions). Tool-agnostic and free to reuse. Nothing to buy.