Methods, Guides, and Strategic Insights. Evidence-based resources to elevate your research practice.
AI is not going to take your job, but it is absolutely going to change it. Understanding what LLMs actually are, and are not, is the foundation for using them effectively.
Beyond basic prompting, there are techniques that dramatically improve AI reliability: structured communication, using notes over transcripts, treating models as a committee of raters, and understanding when RAG or fine-tuning makes sense.
The biggest mistake teams make with AI is treating it like a magic black box. Here is a complete, reliable workflow for using LLMs as research assistants while maintaining critical human oversight.
Don't write a report in a vacuum. How to facilitate a workshop that forces your team to own the insights and commit to action.
Transform interview transcripts and observation notes into actionable themes through systematic coding. The difference between an opinion and a finding is whether two people agree.
Good research is not a series of disconnected activities, it is a cohesive process that transforms business questions into actionable insights. This is the map for that journey.
Don't just report averages. How to clean data, visualize distributions, and calculate statistical significance.
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