📋 Backlog Grooming

Identify Duplicates and Stale Backlog Items

Audits a backlog of 30+ items and flags duplicates, near-duplicates, and items that have gone stale based on age and activity. Built for PMs inheriting or cleaning up a bloated backlog.

This prompt runs a 3-pass audit on a backlog: exact-duplicate detection, semantic near-duplicate clustering, and staleness scoring using a 90-day last-activity threshold. Output is a ranked list of archive, merge, and keep recommendations.

When to use this prompt

Use this when your backlog has grown to 80+ items and you suspect at least 20 percent is dead weight. It works best when you paste in titles plus the last-modified date for each item; without dates, staleness detection is guesswork. This is especially valuable for PMs inheriting a backlog from a departing PM, or after a reorg when two teams merge their work queues. The output is recommendations, not commands: sensitive items (customer commitments, compliance) should always get a second human review before archival.

The Prompt

Role: Product Manager Variables: {{backlog_items}}, {{staleness_days}}, {{product_area}} 
You are a product operations specialist running a backlog hygiene audit. I will paste a list of backlog items with their titles, brief descriptions, and last-modified dates. Run a three-pass audit:

Pass 1 — EXACT DUPLICATES: Find items that are clearly the same request (same title or near-identical wording). Group them and recommend one survivor with a 1-sentence merge note.

Pass 2 — SEMANTIC NEAR-DUPLICATES: Find items that describe the same underlying problem in different words (e.g., "fix checkout error" and "payment fails on mobile"). Cluster them and suggest whether to merge, link as related, or keep separate.

Pass 3 — STALENESS SCORE: For each remaining item, assign a staleness score using this rule: last modified >90 days ago = STALE, 30-90 days = AGING, <30 days = FRESH. Recommend one of: ARCHIVE, REVIEW, or KEEP.

Backlog to audit:
{{backlog_items}}

Staleness threshold: {{staleness_days}} days
Product area: {{product_area}}

Output three sections: DUPLICATES, NEAR-DUPLICATES, STALENESS. End with a summary line: "X items recommended for archival, Y for merge, Z for human review." Do not archive items that mention compliance, security, or named customer commitments — flag those for human review instead.

Example Output

DUPLICATES
- PROD-112 "Add dark mode toggle" and PROD-298 "Dark mode preference" — merge into PROD-112, keep earlier comments.
- PROD-445 "Export to CSV" and PROD-501 "CSV download" — merge into PROD-445.

NEAR-DUPLICATES
- Cluster A: PROD-203, PROD-267, PROD-334 all describe slow search. Recommend: keep PROD-203 as parent, link others as related. Root cause likely shared.
- Cluster B: PROD-156 and PROD-412 both mention empty-state UI issues. Keep separate; different pages.

STALENESS
- PROD-078 (last modified 210 days ago) — ARCHIVE.
- PROD-091 (180 days) — ARCHIVE.
- PROD-134 (95 days, mentions SOC2) — REVIEW (compliance flag).
- PROD-287 (45 days) — KEEP, aging.

Summary: 12 items recommended for archival, 5 for merge, 3 for human review.

Recommended Tools

Jira and ClickUp both expose last-modified timestamps and bulk archive actions that make it fast to execute on the audit recommendations. Airtable is the best fit when your backlog lives in a custom ops database, since you can import the output directly as a view with filters for duplicate groups. All three let you bulk-tag items for merge without losing comment history.

Jira Software75/100 ClickUp94/100 Airtable79/100

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Frequently Asked Questions

When should I use this prompt?

Run it quarterly as a hygiene ritual, or immediately after inheriting an unfamiliar backlog. It pays for itself fastest on backlogs over 100 items, where manual duplicate scanning becomes impractical. Do not use it on backlogs under 30 items — you will find duplicates faster by eye. Avoid running it right before sprint planning; the cognitive load of reviewing archive recommendations alongside prioritization decisions leads to rushed choices.

How do I trust the duplicate recommendations?

Treat the EXACT DUPLICATES pass as high-confidence (it is essentially string matching) and the NEAR-DUPLICATES pass as a starting hypothesis that needs human judgment. Always open both candidate items side-by-side before merging, because semantically similar titles sometimes hide different root causes. For staleness, check the comment thread before archiving — an item with zero activity for 200 days may still be blocking a customer you forgot about.