Data Before Prompt

Most “AI for Agile” advice skips the hardest part: getting your data into shape. A clever prompt won’t fix messy inputs. If your backlog, links, and context are thin or inconsistent, the model will produce thin, inconsistent output.

TL;DR → Bad data = bad advice.

I built the Agile AI Playbook to help teams plug AI into existing Agile practices with a simple framework. The bar for useful results isn’t fancy wording—it’s your data.

What “good enough” looks like (minimum viable inputs)

Sprint Planning

• One clear sprint goal

• Top 10 backlog items with IDs/links

• Known dependencies

Daily Standup

• Yesterday / Today / Blocked notes

• Story or task IDs + owner

• Last update timestamp

Retrospective

• Three wins + three challenges (with brief impact notes)

• Agreed actions with owners and dates

Small habit, big payoff:
Keep IDs and links in the prompt, keep language concrete (outcomes, not slogans), and keep recent history handy (last 2–3 sprints). Prompts are reusable; clean context is the multiplier.

Why I built it

The Agile AI Playbook gives teams copy-ready prompts and lightweight playbooks that fit your current rituals—planning, standups, retros—so AI becomes a quiet accelerator, not overhead.

• Human-curated prompts by practice area

• “Where to paste” guidance (Jira, Notion, Confluence, etc.)

• Quality criteria so outputs actually hit the 🎯

Try it free (early access)

Open access through Oct 31 — login only, no card.
👉 prompts.sprintworthy.com

PS: Bring your own tools

SprintWorthy is tool-agnostic and outcome-first. Use any stack; just bring the minimum viable inputs above and watch the quality of AI output jump.