AI-Augmented Research
Experiments with GenAI applications to UX research processes
One Knowledge Hub
System architecture diagram integrating multiple customer knowledge repositories into one interface using multiple MCPs.
Scaling UX Processes with LLM-Powered Workflows
As a research study's scope, number of interviews/transcripts to encode and the number of stakeholders collaborating increased, I began building LLM-powered workflows to scale processes without sacrificing rigor and learn applications of GenAI to UX research in the process. This also enables UX research teams to align with the enhanced speed of execution by PM / Engineering teams with use of AI.
Ask the Report
Background
Often, cross-functional stakeholders have many unanswered questions during the readout or follow-up clarifications after the readout. UX researcher is not always available, may be in a different timezone or busy with another project.
Research Questions
- Would it be possible to create a simple chat-based application that answers any questions on the research report?
Research Approach
To ensure qualitative rigor, I designed a four-stage pipeline:
- CalibrateThe researcher manually codes the first 3 transcripts to define the tagging taxonomy.
- Quality ControlThese human examples are used as few-shot training for the LLM. The script executes, and the researcher audits the tagging boundaries, refining prompts.
- SynthesisThe script calculates tag counts and validates them against raw evidence before committing.
- Ask the ReportVerified highlights and scrubbed transcripts are loaded into the vector index as grounded system context for stakeholder Q&A.
What We Learned
Learnings from this program
Calibration beats instruction
Simply writing rules in a prompt is not enough; you must feed the model concrete, human-coded examples.
The bottleneck shifts, it doesn't disappear
While the AI reduces tagging time, the human researcher must spend more time designing validation schemas and auditing outputs.
Design the system first
Building a local model architecture with structured outputs is essential before running code
Research Artifacts
Assets and frameworks generated to guide other UX researchers on the team:
Methodology
Re-usable methods for each experiment
Prompt Templates
Prompts used
Guidelines & Learnings
Best practices document
Experimental Trials
A portfolio of experiments exploring generative AI capabilities across qualitative tasks.
Experiment 01: Secondary Research
Experiment 02: Ask the Report
Experiment 03: Figma -> Study Guide
Experiment 04: Calibrate-then-Replicate
Experiment 05: Compare personas
Experiment 06: One Knowledge Hub
Research Impact
The structural, organizational, and methodological contributions generated by this program.
Organizational Alignment
- Best practices and learnings
Methodological Value
- Prompt templates