In a recent Larridin demo, a principal engineer asked, “Can you distinguish a team doing advanced Google search every day from real proficiency?” High-frequency AI usage and genuine AI proficiency are not the same thing — and the difference is measurable.
Key Takeaways
- Using prompt volume as a proxy for skill can hide meaningful differences in how effectively teams use AI.
- Larridin’s Proficiency Insights looks beyond volume, using Prompt Quality and Use Case Diversity to show how effectively teams are using AI.
- For engineering teams, proficiency should be read alongside results. Verification, rework, quality, and durability help show whether stronger AI use is translating into dependable work.
What “Advanced Google Search” Means in This Context
The principal engineer wasn’t questioning whether frequent AI use has value. They were asking whether frequency alone can distinguish basic use from stronger capability. It can’t.
Someone can use an AI tool every day for a narrow set of tasks like looking up information. Another person may use the same tool across more complex workflows and apply it more effectively. Both look active in an adoption metric.
That’s the distinction between AI adoption and AI proficiency. Adoption shows whether people are using AI. Proficiency measures how effectively they are using it.
Larridin’s product documentation makes the distinction similarly: adoption measures presence, engagement measures consistency, and proficiency measures effectiveness.
What to Measure Instead of Prompt Volume
Prompt Quality
Prompt volume tells you how often someone interacts with AI, not whether those interactions are effective.
Larridin’s Proficiency Insights includes Prompt Quality as one of the underlying measures used to assess the sophistication and effectiveness of AI use. The goal is to distinguish simple activity from stronger use without treating the number of prompts as a proxy for skill.
Use-Case Diversity
Proficiency also depends on how AI is applied across work.
Larridin’s Use Case Diversity measure looks at breadth of application. Someone using AI repeatedly for one narrow task is at a different proficiency level from a team applying it effectively across multiple relevant workflows.
More use cases aren’t automatically better. The useful signal is whether AI is being applied effectively where it fits the work.
Verification and Rework
For engineering teams, proficiency data also needs outcome context. An engineer can generate a large amount of AI-assisted work quickly and still spend substantial time correcting or validating it.
Verification Discipline looks at how rigorously engineers check AI-generated code before trusting it, including signals from reviews, testing, and what happens after the change ships.
Rework provides another useful check. If AI activity is high but correction work is also high, volume doesn’t give leadership a complete picture.
Quality and Durability
The same principle applies downstream. Code durability, reverts, incidents, and other quality measures can help engineering leaders determine whether AI-assisted work is holding up after it ships.
They show whether AI-assisted work is producing dependable engineering outcomes, not how proficiently someone used AI.
Larridin’s Proficiency Measurement
Larridin’s AI Fluency capability includes Proficiency Insights, which uses Prompt Quality and Use Case Diversity to show how effectively teams are using AI. Leaders can view proficiency distributions over time and compare patterns across departments.
For engineering teams, those signals can also be read alongside delivery, verification, rework, quality, and durability.
Larridin is also developing the AI Proficiency Score (APS), currently in beta, which summarizes the depth and sophistication of AI use into a single score.
Frequently Asked Questions
Does high AI usage mean high proficiency?
No. High usage shows frequency, not effectiveness. A team can use AI often without demonstrating strong proficiency.
What does Larridin’s AI Proficiency Score measure?
The beta AI Proficiency Score measures the depth and sophistication of AI usage. Proficiency Insights also uses Prompt Quality and Use Case Diversity to show how effectively AI is being used across different workflows.
Can proficiency measurement show where teams need help?
Yes. Larridin measures AI fluency across teams and roles and shows how proficiency is distributed over time. That can help leaders identify stronger teams as well as areas where additional enablement may be useful.
Should engineering leaders use proficiency as a standalone performance metric?
No. Proficiency provides one view of how effectively AI is being used. Engineering leaders should read it alongside delivery, quality, verification, rework, and durability rather than treating any single score as a complete measure of performance.
Measure How Well Teams Use AI, Not Just How Often
Larridin’s AI Fluency capability helps organizations distinguish AI activity from AI proficiency so leaders can see where stronger capability is developing and where teams need support.
Book a discovery call to see how AI proficiency is developing across your organization.