Purpose: The Post-Launch Retrospective identifies what worked, what didn’t, and what should be improved after a product or feature release, enabling continuous feedback and faster iteration loops.
Design Thinking Phase: Implement
Time: 45–60 min session + 1–2 hours analysis
Difficulty: ⭐⭐
When to use:After releasing a new feature or product to usersTo assess team collaboration and cross-functional efficiencyTo feed real-world insights back into the design and dev cycle
What it is
The Post-Launch Retrospective is a structured session used after a product or feature goes live. It captures team reflections, user feedback, and performance insights to refine future work. It’s a bridge between delivery and ongoing improvement — crucial in agile and lean UX environments.
📺 Video by Simon Sinek. Embedded for educational reference.
Why it matters
Shipping is only the midpoint. Product teams often stop short of learning from real-world usage. Post-launch retrospectives sharpen the feedback loop, reduce rework, and build design maturity. By examining what failed or thrived, teams create a data-informed baseline for future iterations. Critically, this method empowers design voices in operational conversations — proving UX is ongoing, not a handoff.
When to use
- After the first 2–4 weeks of a release being live
- After a high-risk launch, like a checkout flow redesign or onboarding overhaul
- When user feedback diverges from predicted outcomes
Benefits
- Rich Insights: Helps uncover user needs that aren’t visible in metrics.
- Flexibility: Works across various project types and timelines.
- User Empathy: Deepens understanding of behaviours and motivations.
How to use it
1. Schedule a 45–60 minute session within 2–4 weeks post-launch. Include designers, PMs, engineers, and optionally, CX teams.
2. Set ground rules: psychological safety, curiosity over blame, actionable insights.
3. Use a whiteboard or FigJam template with columns: "What worked", "What didn’t", "What to improve".
4. Ask participants to write stickies individually for 5–10 minutes, then cluster them thematically as a group.
5. Dig into metrics and qualitative feedback (support tickets, NPS verbatims, FullStory sessions) to augment reflections.
6. Prioritise improvement actions based on impact vs effort. Document decisions and assign follow-ups.
Example Output
Retrospective Summary – New Checkout Flow (Fictional)
- What worked: Fewer abandoned carts (dropoff fell from 27% to 16%), clearer price breakdown, dev delivery on time
- What didn’t: Mobile error recovery unclear, address autofill lag on Android, new promo code logic confusing
- Actions: Rewrite coupon failure microcopy, re-test address entry on Samsung Galaxy, explore tooltip on error behaviour
Common Pitfalls
- Blame culture: Skip the engineer-vs-product finger pointing. Focus on system gaps, not people.
- Skipping data: Avoid treating the session as an opinion dump. Use actual support, usage, and analytics evidence.
- Burying insights: Make sure action items are prioritised and visible. Don’t “file and forget” retro boards.
10 Design-Ready AI Prompts for Post-Launch Retrospective – UX/UI Edition
How These Prompts Work (C.S.I.R. Framework)
Each of the templates below follows the C.S.I.R. method — a proven structure for writing clear, effective prompts that get better results from ChatGPT, Claude, Copilot, or any other LLM.
C.S.I.R. stands for:
- Context: Who you are and the UX situation you're working in
- Specific Info: Key design inputs, tasks, or constraints the AI should consider
- Intent: What you want the AI to help you achieve
- Response Format: The structure or format you want the AI to return (e.g. checklist, table, journey map)
Level up your career with smarter AI prompts.Get templates used by UX leaders — no guesswork, just results.Design faster, research smarter, and ship with confidence.First one’s free. Unlock all 10 by becoming a member.
Prompt Template 1: “Summarise UX impact from a recent product launch:”
Summarise UX impact from a recent product launch
Context: You are a UX researcher preparing a retrospective on a new feature rollout.
Specific Info: The feature involved [user onboarding enhancements], launched [2 weeks ago], with access to [usage analytics, support feedback, session recordings].
Intent: Provide a summary of UX wins, issues, and opportunities post-launch.
Response Format: Return a summary in three parts: “Highlights”, “Pain Points”, and “Next Design Actions”.
If data is missing, ask what sources are available (e.g. survey, NPS, app store reviews).
Then, suggest one design insight to further explore.
Prompt Template 2: “Map retrospective themes into an action matrix:”
Map retrospective themes into an action matrix
Context: You are facilitating a team retrospective post-launch of [checkout flow redesign].
Specific Info: Key takeaways span usability wins, error handling gaps, and implementation delays.
Intent: Turn themes into clearly prioritised actions.
Response Format: Return a 2x2 action matrix (Impact vs Effort) summarising actions by quadrant.
If themes feel too vague, ask for contextual examples.
Then, recommend how to share the actions with stakeholders.
Prompt Template 3: “Analyse qualitative support feedback post-launch:”
Analyse qualitative support feedback post-launch
Context: You are a UX lead reviewing user support tickets after launch of [billing dashboard update].
Specific Info: You have [50+ ticket excerpts] and flagged trends around [user confusion and missing invoice details].
Intent: Extract UX learning opportunities from real user complaints.
Response Format: Categorise issues by theme, with counts and a design idea for each.
If the ticket volume is large, ask how the data is grouped (by severity, date, etc.).
Then, suggest how to visualise this insight for prioritisation.
Prompt Template 4: “Optimise mobile pain points identified post-release:”
Optimise mobile pain points identified post-release
Context: You are a product designer reviewing post-launch tap and rage-click data for [mobile nav pattern].
Specific Info: Users struggled with [nested menus] and [scrollable nav v/s fixed headers].
Intent: Recommend UI refinements to reduce friction and boost discoverability.
Response Format: Provide UX suggestions in a table: Problem, Root Cause, Design Adjustment.
Ask for mobile platform breakdown (iOS vs Android) if that could shift recommendations.
Then, suggest a quick usability test plan for validation.
Prompt Template 5: “Extract design learnings from feature adoption metrics:”
Extract design learnings from feature adoption metrics
Context: You are a UX strategist looking at adoption rates 3 weeks after releasing [dark mode feature].
Specific Info: Adoption is at [18%], with user segments showing [high dropoff on mid-tier Android devices].
Intent: Understand the barriers to adoption and design responses.
Response Format: List insights in Problem → Hypothesis → Design Direction format.
If device data’s sparse, ask what cohorts can be compared instead.
Then, suggest when to rerun this analysis after design changes.
Prompt Template 6: “Create a UX storytelling narrative for stakeholders:”
Create a UX storytelling narrative for stakeholders
Context: You are presenting a retrospective to executives post-launch of [global loyalty program app].
Specific Info: Feedback shows [strong UX approval in Australia; low engagement in UK cohort].
Intent: Build a compelling, data-backed story that communicates UX value.
Response Format: Return a five-part narrative: Problem, Team Actions, Results, Learning, What’s Next.
If regional behaviour data is missing, ask how cohorts are currently segmented.
Then, recommend a single wow-slide visual.
Prompt Template 7: “Generate ‘What to keep / change / try’ board content:”
Generate ‘What to keep / change / try’ board content
Context: You are leading a remote retrospective after a [signup conversion uplift experiment].
Specific Info: Team inputs include metrics, screen recording notes, and heuristic feedback.
Intent: Quickly summarise points into Keep/Change/Try clusters for discussion.
Response Format: Return three bullet lists, each with 3–5 points, including rationale.
Ask which sources the team relied on most to weigh evidence.
Then, suggest a prompt to document follow-on design hypotheses.
Prompt Template 8: “Identify friction patterns from interaction logs:”
Identify friction patterns from interaction logs
Context: You're a design researcher using analytics and clickstream data post-launch of [member dashboard phase 1].
Specific Info: Heatmaps show high dropoff after [“Connect your account” step], especially on tablet devices.
Intent: Understand behavioural roadblocks and inform a design fix.
Response Format: Return findings as: Friction Point → Likely Cause → Hypothetical Fix.
Ask about completion funnel endpoints to better map impact.
Then, recommend a micro-survey to validate why dropoff occurs.
Prompt Template 9: “Craft a retrospective wrap-up post for internal sharing:”
Craft a retrospective wrap-up post for internal sharing
Context: You are a senior designer drafting an internal write-up after delivering [new navigation release].
Specific Info: The process involved [three design sprints and input from accessibility leads].
Intent: Summarise lessons and keep internal design visibility high.
Response Format: Return a brief blog-style post with Title, Summary, Highlights, and Thank You section.
Ask if visuals like before/after mockups are available.
Then, offer three Slack headlines to promote the post.
Prompt Template 10: “Propose follow-up research methods post-launch:”
Propose follow-up research methods post-launch
Context: You’re planning the next research phase after shipping [AI-powered search in product catalogue].
Specific Info: You’ve seen [higher engagement but mixed comments on precision].
Intent: Identify qualitative and quantitative next steps to understand behaviour.
Response Format: Recommend 2–3 research methods with when/how to deploy each.
Ask when future design changes are expected to time suggestions appropriately.
Then, suggest how to include customer support in ongoing feedback loops.
Recommended Tools
- Lookback or Dovetail – for continuous user feedback management
- Maze or UsabilityHub – to rerun post-launch UX tests
- FigJam – for collaborative retro boards and action plans
- FullStory or Hotjar – to identify behavioural patterns at scale
