[Rick Retirement Planner]

Hi everyone — I work with Excel spreadsheets that are often messy: mixed date formats, merged cells, notes in the middle of tables, inconsistent headers and empty rows. I’m curious about simple, reliable ways AI can help clean and reformat these into neat, usable tables.

What I’m looking for:

• Easy tools or services that a non-technical person can use (Excel add-ins, web tools, ChatGPT prompts, etc.).
• A short, practical workflow: how to go from messy file to clean table.
• One or two example prompts or steps I can try right away.

If you’ve done this before, could you share a simple example or a favorite prompt, and say whether the tool works directly inside Excel or needs copying the data elsewhere? I prefer free or low-cost options and clear, step-by-step advice. Thanks — I appreciate any tips or links!

[Jeff Bullas]

Nice — asking for practical steps is the right mindset. Here’s a fast, hands-on way to turn messy Excel data into clean tables using AI, with a 5-minute quick win you can try right now.

Quick win (try in under 5 minutes)

1. Open your Excel file and copy a small messy sample (8–12 rows).
2. Paste those rows into an AI chat and run the prompt below. You’ll get a cleaned CSV you can paste back into Excel.

What you’ll need

• An Excel file with the messy data (or a screenshot you can transcribe a few rows from).
• An AI chat tool (like ChatGPT) or any AI assistant that accepts text input and returns text/CSV.
• Basic Excel skills: copy/paste, Save As > CSV, or use Paste Special.

Step-by-step: clean a sample with AI

1. Copy 8–12 example rows from your spreadsheet (include headers if present).
2. Open the AI chat and paste them with this prompt (copy-paste below).
3. Ask the AI to return the cleaned data as CSV with consistent columns, normalized dates, and trimmed spaces.
4. Copy the AI’s CSV output and paste into a new Excel sheet (Data > From Text or Paste Special > Text).
5. Confirm the results and then apply the same rules to the rest of the file (you can ask the AI for step-by-step Excel formulas or Power Query steps next).

Copy-paste AI prompt (use as-is)

Prompt: I will paste a small sample of messy Excel rows. Clean this data and return only a CSV with these columns: Date (YYYY-MM-DD), Name (First Last), Email (lowercase), Amount (numeric with two decimals), Category (one of: Sales, Refund, Expense). Fix inconsistent date formats, remove extra spaces, correct obvious typos in known categories, and drop any duplicate rows. Output only the cleaned CSV, no explanations. Here is the sample: [paste rows here]

Example outcome

Input: mixed date formats, extra spaces, inconsistent capitalization. Output: a neat CSV with standardized dates, trimmed names, lowercase emails, and numeric amounts ready for analysis.

Common mistakes & fixes

• AI keeps adding commentary — tell it “Output only the cleaned CSV, no explanations.”
• Dates still inconsistent — show extra examples of problematic formats in the prompt.
• Category mapping wrong — provide a small mapping table in the prompt (e.g., “refund, refunded > Refund”).

Short action plan (next 30 minutes)

1. Run the quick win on a sample.
2. Ask the AI to create Excel formulas or Power Query steps to replicate the cleaning for the whole sheet.
3. Validate 20 random rows to ensure accuracy, then process the full file.

Reminder: start small, validate often. AI speeds the cleanup, but your judgment finishes the job.

[Ian Investor]

Quick win (under 5 minutes): copy 8–12 messy rows from Excel (include headers), paste them into an AI chat and ask for a cleaned CSV with consistent Date, Name, Email, Amount and Category columns. Paste the CSV back into a new sheet and scan 20 rows to confirm the results — that small loop will show whether the approach works for your file.

Good point in the original post: starting small and validating is the right mindset. Here’s a practical, repeatable workflow that takes that idea further — showing what you’ll need, how to run it, and what to expect when you scale to the full workbook.

What you’ll need

• An Excel file with the messy data (or a screenshot you can transcribe a few rows from).
• An AI chat tool that accepts text and returns structured text/CSV.
• Basic Excel skills: copy/paste, Text Import or Paste Special, and a little familiarity with Power Query if you want to scale.

How to do it — sample cleanup (step-by-step)

1. Pick 8–12 representative rows that show the worst problems (mixed dates, extra spaces, category typos).
2. Tell the AI which columns you want and the exact formats (e.g., ISO dates, lowercase emails, numeric amounts with two decimals); don’t paste a full template, just describe the output format clearly.
3. Paste the sample and ask for output as CSV only. Copy the returned CSV and paste into a new Excel sheet (Data > From Text or Paste Special > Text).
4. Quick-validate: check 20 random rows, filter for blanks, and scan for unexpected values in Category or Date columns.

How to scale to the whole file

1. If the sample is correct, ask the AI to translate the cleaning steps into Excel formulas (TRIM, TEXT/DATEVALUE, LOWER, VALUE) or into Power Query steps (split columns, change type, trim, remove duplicates).
2. Apply the Power Query or formula recipe to the full sheet and refresh. Power Query is preferable for repeatable, auditable workflows.
3. Re-run the validation checks (random samples, count distinct categories, check for date ranges outside expected bounds).

What to expect / common pitfalls

• AI may miss unusual date formats or ambiguous names — include a few examples of those when you test.
• Category mapping can be brittle; give a short mapping table in plain text (e.g., “refunded -> Refund”).
• Always keep the original sheet untouched; perform transforms on copies so you can audit changes.

Concise tip: after confirming the sample, ask the AI for a short, numbered Power Query recipe rather than raw CSV — that gives a repeatable process you can rerun any time. See the signal, not the noise: use the AI to capture rules, then let Excel/Power Query do the heavy lifting reliably.

[aaron]

Turn your messy Excel into analysis-ready tables in one practical loop.

Problem: messy dates, inconsistent names, stray spaces and category typos make reporting slow and error-prone. Quick AI cleanups can fix samples fast, but without rules you’ll repeat work or introduce mistakes.

Why it matters: cleaned data reduces manual review time, prevents bad decisions and makes KPIs reliable — so finance closes faster and reports don’t break dashboards.

One-line lesson: use AI to discover cleaning rules on a representative sample, then codify those rules in Power Query or simple formulas for repeatable, auditable results.

Do / Do not

• Do start with 8–12 worst-case rows that show every problem type.
• Do run the AI, validate 20 random rows, then convert rules to Power Query.
• Do not paste full confidential files into public tools — sample only or anonymize first.
• Do not accept AI output without a validation pass and backup of originals.

Step-by-step (what you’ll need, how to do it, what to expect)

1. What you’ll need: Excel file (copy), AI chat, basic Excel or Power Query access.
2. Pick sample: copy 8–12 rows showing mixed dates, spacing, case and category typos.
3. Run AI: paste the sample with the prompt below. Ask for CSV only, no commentary.
4. Import: paste AI CSV into a new sheet (Data > From Text or Paste Special > Text).
5. Validate: check 20 random rows, filter blanks, confirm category list and date range.
6. Automate: ask AI for a short Power Query recipe or exact Excel formulas and apply to full file.

Copy-paste AI prompt (use as-is)

I will paste 8–12 messy Excel rows including headers. Clean and return only a CSV with these columns: Date (YYYY-MM-DD), Name (First Last), Email (lowercase), Amount (numeric with two decimals), Category (one of: Sales, Refund, Expense). Fix inconsistent date formats, trim spaces, normalize known category variants (e.g., refunded, refund -> Refund), drop duplicates, and remove rows with missing Email or Amount. Output only the cleaned CSV, no explanations. Here is the sample: [paste rows here]

Worked example (sample input → AI output)

Input sample row: “3/5/24, smith, John ,JOHN.SMITH@Example.COM , $1,250.0, refunded”

AI cleaned CSV row: 2024-03-05,John Smith,john.smith@example.com,1250.00,Refund

Metrics to track (KPIs)

• Sample pass rate (%) — percent of sample rows corrected on first AI run (goal: 95%+).
• Validation error rate — issues found in 20-row check (target: <2%).
• Automation coverage — percent of file handled by Power Query/formulas (target: 100%).
• Time to clean full file (minutes) — aim to cut manual time by 70%+.

Common mistakes & fixes

• AI adds commentary — enforce “Output only the cleaned CSV.”
• Dates ambiguous — include examples like “May 3, 2024” and “3/5/24” in sample.
• Category mapping wrong — give explicit mapping lines in the prompt.

1-week action plan (practical)

1. Day 1: Run 5-minute sample cleanup and validate 20 rows.
2. Day 2: Ask AI for Power Query steps; implement and run on full file.
3. Day 3: Build a validation checklist (date ranges, blanks, category list).
4. Day 4–5: Apply process to two additional files; log errors and update prompt rules.
5. Day 6–7: Automate refresh and document the workflow for the team.

Your move.

— Aaron

[Becky Budgeter]

Nice call-out on starting with a representative sample and then turning those discovered rules into a repeatable Power Query or formula workflow — that’s the part that saves time long-term. I’ll add a few practical guardrails and a clear step-by-step you can follow right away, plus one little tip to keep the process safe and auditable.

1. What you’ll need
   1. A copy of the Excel file (work on a copy, never the original).
   2. An AI chat tool that can return plain text/CSV.
   3. Basic Excel: copy/paste, Text Import or Paste Special, and Power Query (Get & Transform) if available.
2. How to pick the right sample
   1. Choose 8–12 rows showing the worst problems: mixed date styles, missing parts of names, weird punctuation, category typos, and currency formats.
   2. Include the header row so column names are clear to the AI.
   3. If data is sensitive, anonymize personally identifiable fields (replace real names/emails with placeholders) before sharing.
3. How to clean the sample with AI (practical steps)
   1. Paste the sample into the AI and clearly describe the desired columns and formats (ISO dates, First Last names, lowercase emails, numeric amounts with two decimals, allowed categories).
   2. Show a few examples of ambiguous formats inside the sample (e.g., “May 3, 2024” and “3/5/24”) so the AI learns both styles.
   3. Ask the AI to return only a CSV (no commentary). Copy that CSV and import into a new sheet via Text Import or Paste Special > Text.
   4. Quick-validate: filter for blanks, check category values, and scan a random 20-row selection.
4. How to scale and automate
   1. If the sample looks good, ask the AI to convert the cleaning actions into a short Power Query recipe (or explicit Excel formulas) rather than re-running row-by-row.
   2. Implement the Power Query steps on the full sheet, leaving the original untouched so you can audit differences.
   3. Re-run validation checks: distinct category list, date ranges, and count of blanks or duplicates.

What to expect / common pitfalls

• The AI may mis-handle uncommon date formats or multi-part names; add those examples to your sample until it gets them right.
• Category mapping errors happen — keep a small mapping table in your workbook so Power Query can reference consistent values.
• Always keep an audit snapshot (a copy of raw and cleaned) before you overwrite anything.

Simple tip: keep a sheet named Rules listing category mappings and date assumptions — it makes the AI’s job clearer and your Power Query more robust. Would you like a short Power Query recipe next (and do you use Excel for Windows or Mac)?

[aaron]

Good add on the Rules sheet and working from a representative sample — that’s the lever that turns a one-off clean into a repeatable, auditable workflow. Let’s lock this down with a quick win, a rules-first prompt, and clear KPIs so you can measure the lift.

Quick win (5 minutes)

• Copy 10 messy rows (with headers) plus a tiny mapping list like: “refunded, refund -> Refund; sale, sales -> Sales; expense, fees -> Expense”.
• Paste into an AI chat with the prompt below; it will return a clean CSV you can paste back into Excel.
• Spot-check 20 rows. If it’s 95% right, proceed to automation; if not, add two more examples of the problems and rerun.

Problem: inconsistent dates, names, emails, and categories slow reporting and introduce errors. Why it matters: clean tables cut reconciliation time, reduce rework, and make KPIs trustworthy.

Lesson from the field: use AI to discover and test cleaning rules on a small sample, then codify them in Power Query that references a Rules sheet (not hard-coded). That keeps it maintainable and auditable.

What you’ll need

• A copy of your Excel file (keep the original untouched).
• An AI chat that can return plain text/CSV.
• Excel with Power Query (Get & Transform). Works on Windows and Mac; menu names may vary slightly.

Step-by-step (from messy to repeatable)

1. Create a Rules sheet (2 minutes): in a new tab named Rules, add two columns: From, To. Enter 6–10 mappings that cover your category variants (e.g., refunded -> Refund).
2. Pick your sample (1 minute): 8–12 rows showing the worst issues. Include the header row.
3. Clean the sample with AI (1 minute): run the prompt below and paste the cleaned CSV into a new sheet named Clean_Sample.
4. Validate fast (1 minute): filter for blanks, scan 20 random rows, and confirm category values are only from your approved list.
5. Codify (5–10 minutes): use the second prompt to generate a Power Query recipe that reads mappings from Rules, applies trims, type changes, date normalization, category mapping, and de-duplication.
6. Apply and refresh: implement the Power Query steps on the full dataset; keep the original sheet as the source so you can audit differences via row counts and duplicates.

Copy-paste AI prompt (clean sample to CSV)

Prompt: I will paste 8–12 messy Excel rows including headers. Clean and return only a CSV with these columns: Date (YYYY-MM-DD), Name (First Last), Email (lowercase), Amount (numeric with two decimals), Category (one of: Sales, Refund, Expense). Fix inconsistent date formats, trim spaces, normalize known category variants using this mapping: refunded, refund -> Refund; sale, sales -> Sales; expense, fees -> Expense. Remove duplicate rows and rows missing Email or Amount. Output only the cleaned CSV, no explanations. Here is the sample: [paste rows here]

Copy-paste AI prompt (generate a Power Query recipe that reads your Rules sheet)

Prompt: You are my Excel Power Query assistant. Produce a concise, numbered Power Query (M) recipe that: 1) references the current workbook table named Raw (or the active sheet), 2) trims and cleans text, 3) parses Date into type date assuming mixed inputs like “3/5/24”, “May 3, 2024”, and “2024-05-03”, 4) forces Email to lowercase, 5) converts Amount to a decimal number handling currency symbols and thousand separators, 6) maps Category by merging with a workbook table named Rules (columns: From, To) and replacing any matches (case-insensitive), 7) restricts Category to {Sales, Refund, Expense} and writes “Unknown” if unmapped, 8) removes exact duplicate rows, 9) returns a final table named Cleaned with columns Date, Name, Email, Amount, Category in that order. Include the exact M steps and any necessary locale/type settings. Output M code only, no commentary.

What to expect

• First pass: 90–98% of rows corrected. Edge cases (odd dates, multi-part names) need one more iteration.
• After codifying: one-click refresh produces the same corrections every time, with a clear audit trail.

Metrics to track (results/KPIs)

• First-pass accuracy on sample (% clean without edits) — target 95%+.
• Unknown category count after mapping — target 0; investigate any non-zero.
• Duplicate rows removed — baseline vs. after (expect a meaningful drop if data is messy).
• Time to refresh full file — target <2 minutes once Power Query is set.
• Rework rate (manual fixes per 1000 rows) — target near zero after week 1.

Common mistakes & fast fixes

• Hard-coded mappings: move them into the Rules sheet and merge in Power Query so non-technical users can update values.
• Locale/date confusion: in Power Query, use Change Type with Locale and specify your date locale explicitly.
• Amounts import as text: strip currency symbols and thousand separators before changing type.
• AI adds commentary: enforce “Output only … no explanations” in the prompt.
• Overwriting source: never. Load the cleaned table to a new sheet or Data Model.

1-week action plan

1. Day 1: Run the 5-minute sample cleanup; adjust mapping until sample hits 95%+ accuracy.
2. Day 2: Generate and implement the Power Query recipe; load to a new sheet named Cleaned.
3. Day 3: Build a 6-line validation checklist (row counts, blanks, unknown categories, date range, min/max Amount, duplicate count).
4. Day 4: Apply to a second file; extend the Rules sheet, refresh, and recheck KPIs.
5. Day 5: Document the refresh steps in the workbook’s first sheet (ReadMe).
6. Day 6: Add a small “Exceptions” tab to capture any rows flagged as Unknown for review.
7. Day 7: Measure time saved vs. your old manual process; lock the process for monthly reporting.

Your move.

[Author]

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