Many teams now use AI to automate routine tasks. It processes large volumes of data quickly, improves several workflows, and reduces manual errors. But the results you get are only as good as the prompts you write. This article covers the top 5 AI prompts for user research note-taking and how to use them.
We'll explain why note-taking matters during user research and how AI fits into the process. Whether you're working on meeting notes or research notes, you'll get practical tips you can apply right away. With examples of the prompts we use for interview notes, you'll know which ones fit your needs.
Read on to learn how to use AI for user research note-taking and which prompts work best.
The Importance of User Research Note-Taking
Note-taking plays a crucial role in any user research process. It allows you to capture vital information during user testing or interviews that will support the qualitative data you gather. Furthermore, capturing real-time data gives you detailed insights into the participants' feedback, actions, and non-verbal cues.
By developing your note-taking skills, you'll know how to organize key data and extract information regarding areas of improvement and pain points by grouping similar themes and behaviors. Once you've captured this information, you can use it to advance your current user research or as reference material for future projects.
Note-taking sounds simple, but it's hard to stay present in an interview while writing down the participant's responses. Even if you're good at multitasking, taking notes while talking can leave you with gaps and misread important data. That weakens the research and can lead to wrong decisions and product changes.
For this reason, many researchers have begun utilizing AI to automate this process and enhance research outcomes.
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AI Prompts for User Research Note-Taking
Now that the value of research notes is clear, here are the top 5 AI prompts for user research note-taking. The results you get depend on the prompts you write. The most common mistake we see is expecting AI to work well with no input from you.
We've tested many note-taking prompts, so here are the ones that gave us the best results:
- Summarizing research calls
- Extracting insights
- Sentiment analysis
- Transcription
- Identifying key pain points and areas of improvement
Summarizing research calls
The first prompt covers summarizing research calls. If you've run a user interview or usability test, you know the conversation doesn't always stay on the key points. Your notes can also lack context, which makes it harder to see the bigger picture and pull out insights.
AI can summarize the main points of your notes without dropping anything important. You can adjust the prompt to your research goals, but give it context first. Include as much detail as you can about the participant, the research objectives, and the product or service being tested.
Here's an example of what an AI prompt for summarizing research calls should look like:
"Summarize the key findings from a user research call with [participant information] using [product/service] for the first time."
Extracting insights
The next prompt covers extracting insights. The goal of user research is to gather information you can use to make data-driven decisions. Even after summarizing a call, pulling out actionable insights can be hard.
You can instruct the AI to generate and organize clear insights from large amounts of data. Here's a simple prompt for that:
"Generate insights from the following notes and organize them in bullet points"
Image by Tung Nguyen from Pixabay
Sentiment analysis
Another key part of user research is sentiment analysis. It analyzes text to determine the emotional tone of a message. The three main labels are positive, negative, and neutral, which tells you more about how the user feels about the product.
Here's a prompt for that:
"Analyze the following text to determine the overall sentiment. Categorize the sentiment as positive, negative, or neutral, and highlight any specific phrases that indicate the user's emotional state. Additionally, provide a brief summary of the user's emotional responses throughout the session."
If you want to break down this prompt into multiple sections so you can focus more on a specific segment, you can try the following examples:
- "Determine the overall sentiment of the user during the session."
- "Identify specific points where the user expressed frustration or dissatisfaction."
- "Highlight moments where the user provided positive feedback or seemed satisfied with the product."
- "Note any neutral or indifferent reactions the user had during the research."
Transcription
Many researchers record their interviews or take audio notes. In those cases, you have to transcribe the recordings before you can use them. The problem is that a transcript can turn into a bulky, unstructured block of text. When that happens, you can prompt the AI to organize your transcript:
"Organize the following transcript by separating the paragraphs and structuring the conversation into clear sections. Remove filler words and irrelevant tangents, ensuring that the final output is concise and easy to follow."
Identifying key pain points and areas of improvement
The last prompt helps you identify key pain points and areas of improvement. Once you've structured your transcripts, summarized the findings, and extracted insights, you can point the AI at your main research objectives. For example, use this prompt to identify pain points:
"Identify customer pain points when using [specific product or service] and come up with solutions that address those issues."
For key areas of improvement, use a similar prompt:
"Identify the key areas of improvement when using [specific product or service] and come up with optimization solutions."
Tips and Tricks for User Research Note-Taking
Beyond the prompts, here are practices we've found useful for AI note-taking. Adopt these as you start using AI for interview notes:
- Be specific: be as thorough and specific as you can. Detailed instructions get you more complete and detailed results than general questions.
- Grammar: keep your prompts grammatically correct. AI runs on the instructions you give it, so too many errors can confuse it and produce weak answers.
- Include all necessary information: add specific vocabulary or key concepts alongside the notes when they're relevant to the research.
- Remove unnecessary sections: most interviews contain filler, so cut those parts so the AI focuses on the segments that matter most.
Photo by Scott Graham on Unsplash
The Benefits of Using User Evaluation for User Research Note-Taking
Finally, here's how User Evaluation helps with note-taking during user research. The most useful note-taking prompts cover summarizing findings, extracting insights, organizing transcripts, and identifying sentiment and pain points. Doing each of these separately takes a lot of time, so we built a platform that handles them all at once.
Instead of relying on prompts to make sense of your research, you get every feature in one place. Upload your written, video, or audio notes, and User Evaluation transcribes them in over 57 languages and organizes them by speaker. Along with a clear view of speaker timeframes, you can see the emotions behind the user's experience with the "Sentiment Analysis" option.
For extracting information from your notes, User Evaluation uses AI to generate clear, actionable insights alongside their data sources. You can also organize your findings on a Kanban board and add tags and notes for future reference.
Conclusion
AI needs your instructions to produce good results. Write thorough prompts, give it enough information, and keep your grammar clean. Since AI can analyze large volumes of data, the right prompt speeds up research and cuts manual errors.
If you want a single tool with all of these features, try User Evaluation. It's an AI platform for customer understanding: transcribe your content, summarize your calls, and extract insights in a few clicks. Contact us today to see how it can improve your note-taking for user research.