Qualitative data has historically been underutilised because it is expensive to analyse. Reading 500 customer interview transcripts and synthesising themes takes a skilled researcher weeks. A well-structured LLM pipeline can do the same work in hours.

What LLMs are good at in qualitative research

  • Theme extraction — identifying recurring concepts across hundreds of documents.
  • Sentiment classification — not just positive/negative, but nuanced emotional register.
  • Entity extraction — pulling out specific product features, competitor names, job titles.
  • Contradiction flagging — identifying where customer statements conflict with each other or with internal data.

The pipeline structure that works

Chunk → label → aggregate. Split each document into logical segments (per question, per speaker turn). Pass each chunk to the model with a structured prompt that asks for specific outputs. Aggregate outputs across all chunks to identify patterns.

Where the model falls short

LLMs are poor at maintaining context across very long documents and at detecting subtle irony or sarcasm. They also hallucinate when asked to summarise specific numerical claims. Build your pipeline to flag low-confidence outputs for human review.

The analyst is not replaced. They move upstream — designing the research, writing the prompts, and interpreting the aggregated output.