Domain Expertise and AI Agent Cost Efficiency — What the Data Shows — Secrets of the LLM Whisperer

Q: How does domain expertise affect CPST

Novice users average about **$10** CPST. Intermediate users drop to about **$7**. Advanced and expert users stabilize around **$8**. The **30 percent** improvement from novice to intermediate is the steepest decline. Beyond that point, gains flatten.

Q: How much more output do experts generate per prompt

Agents take **9 percent** more actions and deliver **13 percent** more outputs per expertise level. An expert prompt triggers roughly twelve actions and **3,200 words** of output. A novice prompt triggers about five actions and **600 words**.

Domain Expertise and AI Agent Cost Efficiency — What the Data Shows

Figure 1: Domain Expertise and AI Agent Cost Efficiency — What the Data Shows

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Key Takeaway

Cost savings from domain expertise flatten beyond the intermediate level. For the Whisperer archetype, CPST drops 30 percent from novice to intermediate: from about $10 to $7. Advanced and expert users average close to $8 respectively. Investing in beginner to intermediate-level agent skill training may deliver the majority of cost benefits.

Expertise Matters in LLM Use, Up to a Point

Anthropic's analysis of ~400,000 Claude Code sessions revealed a relationship between domain expertise and task success. Novices had a verified success rate of 14.5%. Intermediate and above users achieved verified success rates of 28–33%. Anthropic defined verified success as code pushed to Github, verbal confirmation of success and other concrete completion actions.

More experienced users get the agent to do more per prompt. Agents take nine percent more actions and deliver thirteen percent more outputs per expertise level. Specifically, an expert prompt sets off roughly twelve actions and 3,200 words of output. A novice prompt sets off about five actions and 600 words.

How Expertise Affects Cost Per Successful Task

Expertise improves task outcomes. But can it also boost cost efficiency?

In the simulation CPST was measured across five expertise levels defined by Anthroopic (Novice, Beginner, Intermediate, Advanced and Expert) within each behavioral archetype. The pattern is consistent but the magnitude varies. For the LLM Whisperer, CPST declined by 30 percent at the intermediate level: from about $10 for novices down to $7 for simulated intermediate LLM users. Beyond this point, cost saving gains flatten. Advanced and expert Whisperers average close to $8 respectively. The same flattening pattern holds across all five behavioral profiles.

Why Intermediate Users Get the Most Value

Domain expertise improves task completion, which delivers financial value up to the intermediate level. But deep specialization yields only a modest return on CPST. The expertise-cost flattening pattern means that investing in beginner to intermediate-level AI agent skill training may deliver the majority of benefits, with diminishing returns at advanced and expert levels.

This finding challenges the assumption that more expertise always equals better economics. The truth is more nuanced. Behavioral discipline at the intermediate level may provide more cost efficiency gains than expertise alone.

Frequently Asked Questions

Novice users average about $10 CPST. Intermediate users drop to about $7. Advanced and expert users stabilize around $8. The 30 percent improvement from novice to intermediate is the steepest decline. Beyond that point, gains flatten.

Domain expertise improves task completion, which delivers financial value up to the intermediate level. Deep specialization yields only a modest return on CPST. Behavioral discipline at the intermediate level provides more cost efficiency gains than additional domain depth.

Agents take 9 percent more actions and deliver 13 percent more outputs per expertise level. An expert prompt triggers roughly twelve actions and 3,200 words of output. A novice prompt triggers about five actions and 600 words.

Beginner to intermediate training delivers the majority of cost efficiency benefits. Advanced and expert training provides diminishing returns on CPST. Teams should consider investing in foundational prompt discipline and basic domain knowledge before pursuing deep specialization for agent workflows.

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Domain Expertise and AI Agent Cost Efficiency — What the Data Shows

Expertise Matters in LLM Use, Up to a Point

How Expertise Affects Cost Per Successful Task

Why Intermediate Users Get the Most Value

Frequently Asked Questions

How does domain expertise affect CPST?

Why does expertise stop improving cost efficiency at advanced levels?

How much more output do experts generate per prompt?

What does this mean for training investment?

Why Are Your LLM Costs Rising?