Agile retrospectives are vital for continuous improvement, allowing teams to reflect on their experiences and refine their approaches for future sprints. The LLLL Method – Learned Liked Lacked and Longed For—is a thoughtful and structured way to surface insights. This method helps teams analyze their sprint experiences through four lenses:
- Learned: Discoveries, new ideas, or approaches gained during the sprint.
- Liked: Positive aspects or practices that worked well and should be repeated.
- Lacked: Resources, skills, or elements that were missing but needed.
- Longed For: Aspirations or improvements that could have made the sprint easier or more effective.
By categorizing feedback into these areas, the Learned Liked Lacked Method provides a balanced view of successes, challenges, and opportunities for growth.
Why Use the LLLL Method?
The LLLL Method offers a structured framework for reflection that promotes constructive discussions and actionable outcomes. It ensures that:
- Successes are acknowledged and reinforced.
- Learning is captured and integrated into future practices.
- Gaps are identified for immediate improvement.
- Aspirations are explored to innovate and streamline processes.
This method encourages a well-rounded discussion that goes beyond what went wrong, focusing on growth and future potential.
How the LLLL Method Works
1. Set the Stage
Introduce the four categories to the team and explain their purpose:
- Learned: “What new insights, methods, or approaches did we gain during this sprint?”
- Liked: “What did we enjoy or think worked well?”
- Lacked: “What was missing that would have made the sprint more successful?”
- Longed For: “What did we wish we had to make the sprint easier or more effective?”
2. Collect Feedback
Ask participants to reflect on the sprint and add their feedback under each category. This can be done with sticky notes (physical or virtual) or through a collaboration tool like Miro, MURAL, or Jamboard.
3. Review and Discuss
Go through each category one by one:
- Celebrate what the team Learned and Liked.
- Analyze what the team Lacked and explore ways to fill those gaps.
- Discuss what the team Longed For and consider how these aspirations can be realized.
4. Identify Action Items
Turn the discussion into actionable steps for the next sprint:
- Integrate Learned insights into workflows.
- Repeat or scale Liked practices.
- Address Lacked resources or skills.
- Experiment with or implement Longed For improvements.
Examples of LLLL Insights
Learned
- “We discovered a faster way to run our test suite by parallelizing tasks.”
- “Pair programming improved code quality and team collaboration.”
Liked
- “Our new daily standup format kept us focused and on time.”
- “Stakeholder feedback sessions were more productive and gave clear direction.”
Lacked
- “We didn’t have enough time to complete proper documentation.”
- “A better way to visualize progress in our Kanban board was missing.”
Longed For
- “It would have been easier if we had a tool for automating deployment.”
- “Clearer goals at the start of the sprint would have made prioritization smoother.”
How AI Tools Can Enhance the LLLL Method
AI tools like ChatGPT and Microsoft Copilot can significantly enhance the effectiveness of the LLLL Method, making it easier to gather, analyze, and act on team feedback.
1. Generating Thoughtful Prompts
AI can craft engaging and specific questions to encourage deeper reflection:
Learned:
- “What new techniques or knowledge did you gain during this sprint?”
- “Did we discover any best practices we can use in the future?”
Liked:
- “What did you enjoy about our processes or teamwork?”
- “Which aspects of the sprint felt most effective?”
Lacked:
- “What resources or information would have helped us complete tasks more effectively?”
- “Was there anything that slowed us down because it was missing?”
Longed For:
- “What tools or changes would have made the sprint easier?”
- “Is there something you’ve seen elsewhere that we should try?”
2. Supporting Feedback Collection
AI tools can streamline the process of collecting and categorizing feedback:
- Virtual Boards: Use Copilot to set up a digital collaboration board with pre-labeled columns for Learned, Liked, Lacked, and Longed For.
- Surveys and Polls: AI can help create quick surveys for anonymous input to ensure all voices are heard.
3. Analyzing Patterns
AI can analyze the team’s feedback to highlight trends and priorities:
- “The team Learned a lot about using parallel testing but Lacked proper training on the framework.”
- “There’s consensus that the new standup format was Liked, and several team members Longed For a tool to automate deployment.”
4. Brainstorming Solutions
For items in the Lacked and Longed For categories, AI can help brainstorm actionable solutions:
- Input: “We lacked time for documentation.” AI Suggestion: “Schedule a dedicated documentation session at the end of each sprint.”
- Input: “We longed for an automated deployment tool.” AI Suggestion: “Research and evaluate tools like Jenkins or GitHub Actions to streamline deployment.”
5. Creating Action Plans
AI can summarize discussions and generate clear action items:
- “Learned: Integrate parallel testing into all sprint workflows.”
- “Liked: Continue using the new standup format.”
- “Lacked: Allocate 4 hours in the next sprint for documentation.”
- “Longed For: Assign a team member to evaluate deployment automation tools.”
Using LLLL Insights for the Next Phases
The results of the LLLL Method directly inform the subsequent phases of the retrospective:
Generate Insights
- Explore root causes of Lacked items to understand why they were missing.
- Discuss feasibility and potential benefits of Longed For items.
Decide What to Do
- Create concrete action items for each category:
- Learned: Share and document new knowledge.
- Liked: Plan to repeat successful practices.
- Lacked: Address gaps by allocating resources or changing workflows.
- Longed For: Experiment with or implement new tools or processes.
Conclusion
The LLLL Method provides a balanced and structured approach to the Gather Data phase of retrospectives. By reflecting on what was Learned, Liked, Lacked, and Longed For, teams can celebrate their successes, address challenges, and explore opportunities for improvement.
Streamline Your Retrospectives with Ready-to-Use Templates
Take it a step further with our editable LLLL Template, designed to seamlessly integrate AI-powered insights into your retrospective workflow. Whether you’re working in-person or remotely, this slide deck makes it easy to apply the method, ensure balanced participation, and keep your team focused on actionable outcomes.
Make your retrospectives even smoother with our editable LLLL Method template. This template includes pre-designed sections for each category and actionable tools to ensure productive discussions and meaningful outcomes. This template will save you time, foster engagement, and ensure a productive start to every retrospective.
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