If you use LLMs for work that matters, how do you decide when to trust the output?
Summary
A conceptual guide on deciding when to trust LLM outputs in high-stakes professional contexts like legal, clinical, and financial work, emphasizing the need for critical evaluation skills.
Similar Articles
We’ve been analyzing how people are using LLMs for legal and compliance tasks (GDPR, AI Act, etc.).
Analysis of LLM usage in legal and compliance tasks reveals that models often produce confident but unverifiable citations, raising questions about reliable legal grounding for AI outputs.
Six questions before you add an LLM
The article argues against blindly adopting LLMs and provides six questions to evaluate whether an LLM is appropriate for a given workflow, emphasizing that LLMs trade determinism for flexibility and should only be used when necessary.
Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientists
This paper introduces IntegrityBench, a benchmark for evaluating whether LLMs uphold research integrity when acting as co-scientists under institutional pressure. Findings show frontier models fail roughly 1 in 3 integrity-critical decisions under peak pressure, and that ethical action does not require accurate misconduct classification.
Judge, Retrieve, or Abstain: Uncertainty-Guarded LLM Judging with Provable Risk Guarantees
This paper proposes a risk-controlled framework for using LLMs as judges in factual evaluation, calibrating uncertainty thresholds to maintain a user-specified error rate and routing to retrieval-augmented mode when needed, achieving higher coverage with provable reliability guarantees.
Challenges and Recommendations for LLMs-as-a-Judge in Multilingual Settings and Low-Resource Languages
This paper analyzes the use of LLM-as-a-Judge in multilingual and low-resource settings, finding inconsistent evaluation outcomes and overtrust in LLM judgments, and provides recommendations for better practices.