How Do LLMs Perform on Interpretive Technical Work?
We ran a structured evaluation of leading models against interpretive, source-grounded technical tasks. The results challenged some of our assumptions. We are working toward publishing a report with expanded findings.
- Why generic AI benchmarks may not translate to interpretive domains
- What changes when human expert review is added to model evaluation
- Preliminary signal across commercial and open-source models
How Do LLMs Perform on Interpretive Technical Work?
We ran a structured evaluation of leading models against interpretive, source-grounded technical tasks. The results challenged some of our assumptions. We are working toward publishing a report with expanded findings.
- Why generic AI benchmarks may not translate to interpretive domains
- What changes when human expert review is added to model evaluation
- Preliminary signal across commercial and open-source models
Why We Ran This Evaluation
We deliver technical projects for energy companies. Like many organizations, we needed to understand which AI models are reliable enough for interpretive, source-grounded work.
We looked for decision-relevant benchmarks and didn't find what we needed. Most public benchmarks are designed around objective, independently verifiable answers. Subsurface and engineering work is often interpretive, ambiguous, and context dependent.
So, we built an internal evaluation approach and tested a range of models against it.
What It Is
An internal evaluation designed to inform real workflow decisions
A comparison using a human-verified technical Q&A dataset
Preliminary, directional results shared transparently
What It Is Not
Not a vendor endorsement or model ranking for procurement
Not a commercial product or subscription
What would be most useful to you?
We are planning to expand this work. Your input helps us focus on what matters.