Urgent.News

What's breaking now, across thousands of outlets.

AI

Designing an Auditable LLM-Supported Workflow for Qualitative Thematic Analysis

Large Language Models (LLMs) offer new possibilities for scaling qualitative analysis, but existing applications often provide limited methodological transparency regarding how qualitative methods are translated into computational procedures. This paper presents an auditable and privacy-preserving computational operationalization of inductive and latent Thematic Analysis (TA). This paper first…

We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.

Read the original at arxiv.org →

More in AI

RAG Without the Hype: Make Retrieval Observable, Testable, and Replaceable

How my agent actually finds answers — and what happens when it doesn't Part 5 findings of an experiment: building an LLM-powered support agent with deterministic boundaries.

  • RAG retrieves relevant documents from knowledge base for query answers
  • Deterministic boundaries and testability emphasized for RAG implementation
  • Retrieval being probabilistic leads to failures, mitigated through quality tracking

More from Monday 31 August →