Why Human-Led Codeframe Refinement Is Crucial in Verbatim Coding

Why Human Refinement Still Matters

codeit can quickly extract themes, generate a draft codeframe and assign codes across large volumes of verbatim text. But before you hand the data off to a client or use it to drive important insights, there are a few essential human steps that no AI can replicate: 

1. Sense Checking the Codeframe and Coding

Each code in your codeframe should mean something. And the best way to evaluate that is to view example verbatims associated with each code. Are the verbatims consistent? Do they clearly represent the label they’ve been assigned? If not, it may be time to refine the definitions or reassign examples. This process helps eliminate false positives, identify subtle themes, and surface edge cases - essential for accuracy and credibility. 
 

2. Editing and Renaming Labels for Nuance

Labels are more than just placeholders. They frame the story you're telling through your data. As you explore the content within each code, it becomes clear that some AI-generated labels don’t quite reflect the nuance of the actual verbatims. Human review allows you to rename and clarify these labels, ensuring they truly represent the underlying sentiment, intent, or topic. This subtle act of renaming can make the difference between a bland analysis and a genuinely insightful one. 
 

3. Aligning with Research Objectives and Client Requirements

A good codeframe isn’t just internally coherent - it’s fit for purpose. Whether you’re running brand tracking, CX analysis, or exploratory qual research, the codeframe needs to map onto your research objectives. Human reviewers are essential for checking that the codes surface the themes clients care about and support the outcomes they’re trying to achieve. AI can propose a structure, but only a researcher or analyst can say whether it’s the right one. 

4. Adjusting Granularity: The Art and Science of Coding

One of the biggest challenges in building codeframes is determining the right level of granularity for each code. Sometimes, you want very broad themes to give you a high-level overview. Other times, you need to break those themes down into sub-topics to understand nuance or detail. The tricky part? There’s no one-size-fits-all answer - what’s “too detailed” in one project might be “not detailed enough” in another.

Even worse, levels of granularity usually vary within a codeframe. For example, in a product test, it may be fine to include a high level code for “Packaging” but a general code for “Flavor” my not be granular enough to be actionable.  
 

That’s why our new tool doesn’t make rigid decisions for you. Instead, codeit suggests sub-themes where it sees potential, and allows you to decide whether to expand or collapse them. This flexibility means you can tailor your codeframe to suit the needs of your study - zooming in or out as needed, without starting from scratch. 

The Bottom Line

codeit combines the efficiency of automation with the insight of human expertise. Our new codeframe refinement tool is designed to bridge that gap - giving you the power to sense-check, relabel, re-structure, and align your codeframe with your research goals. 

AI can get you 80% of the way there. But when it comes to delivering quality insights that clients trust, that last 20% - the human touch - is what makes all the difference.

Try it out for yourself with a free trial of codeit.