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This course is for teams that analyse written feedback from customers, staff, users or other groups. GenAI can organise and summarise comments quickly, but a confident summary does not show whether the themes are clear, the findings are fair or the priorities are supported by what people said.
Participants learn a no-code method for framing an analysis question, preparing feedback safely and developing a clear theme framework. They test how consistently comments are categorised, identify patterns and exceptions, trace important findings to the source comments and prioritise service improvements using transparent reasons.
Current Needs
The challenge is not simply collecting feedback. Teams need a practical way to turn written comments into findings that other people can understand, check and use.
- There is too much to read. Comments may accumulate faster than teams can organise and compare them.
- Themes may depend on the reviewer. Different people—or different prompts—may group the same comments in different ways.
- Simple sentiment misses the reason. Positive or negative labels do not explain what happened, why it matters or what should change.
- Findings may be difficult to defend. A summary may overlook exceptions, minority views, ambiguity or limitations in the feedback set.
- Priorities may be unclear. Decision-makers may not be able to see why one improvement should be addressed before another.
What You Will Achieve
By the end of the course, participants will be able to:
- Frame a useful analysis question and prepare a set of written feedback for GenAI-supported analysis, considering scope, data quality, privacy, potential bias and applicable organisational AI governance requirements.
- Develop, test and refine prompts and theme definitions by comparing categorisation results for clarity and consistency.
- Analyse patterns, differences, relationships, underlying reasons and exceptions in feedback while maintaining neutrality, and verify findings against the source comments.
- Prioritise service improvements and communicate the supporting evidence, limitations, potential bias and reasons for the recommendations.
Training Approach
Participants analyse comments and other feedback to determine improvement priorities. Every material finding must remain connected to the comments behind it, so participants can explain and challenge the analysis rather than rely on a confident GenAI summary.
Short demonstrations introduce feedback preparation, theme design, categorisation and evidence tracing. Participants then experiment with prompts and themes, compare results and improve their analysis through guided practice, peer discussion and feedback.
- Authentic feedback context. Participants work with a realistic, appropriately prepared set of written comments and a defined service question.
- Facilitated modelling. The trainer demonstrates how to define themes, test categorisation and trace a finding to source evidence.
- Guided experimentation. Participants compare prompt and theme versions to identify ambiguity, overlap and inconsistent results.
- Evidence review and reflection. Participants challenge AI-generated findings, look for exceptions and record limitations before recommending action.
- Assessment and transfer. Each participant completes an individual practical assessment covering feedback preparation, prompt and theme refinement, evidence-traced analysis and a prioritised improvement brief.
Participants finish with a practical, explainable approach to using GenAI for qualitative feedback analysis and developing evidence-based approaches for better service.
Module 1: Framing and Preparing Feedback Analysis
- Frame a useful analysis question and define the feedback that will be examined.
- Identify data-quality, privacy, bias and governance issues before using GenAI.
- Practise preparing a feedback set and a simple analysis brief for safe use.
Module 2: Developing and Testing Themes with GenAI
- Create clear theme definitions and prompting instructions for categorising feedback.
- Recognise unclear, overlapping or inconsistent themes and categories.
- Experiment with prompt and theme versions, compare the results and refine them for consistency.
Module 3: Analysing Patterns and Verifying Findings
- Identify meaningful patterns, differences, possible reasons and exceptions in the feedback.
- Maintain neutrality and distinguish supported findings from assumptions or unknowns.
- Practise checking an AI-generated analysis against source comments and recording its limitations.
Module 4: Prioritising and Communicating Service Improvements
- Prioritise service improvements using stated criteria and supporting evidence.
- Communicate limitations, potential bias and areas that need further investigation.
- Practise producing a concise improvement brief with clear reasons and next steps.
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