Case Study: Raj's Capability Testing Protocol — Evaluating New AI Tools Systematically

Why Raj Stopped Reading AI Benchmarks

Early in his AI adoption, Raj made a mistake that cost him time and credibility with his team.

A new AI coding tool released impressive benchmark results — higher than the tool he was using on standard coding benchmarks. He recommended the switch to his team based primarily on those numbers and some enthusiastic coverage in newsletters he trusted.

Two weeks in, three of his developers reported that the tool was producing code that looked correct but had subtle logic errors in a specific class of problems common to their codebase. The benchmark results, it turned out, had been achieved on a standardized problem set that didn't reflect the specific patterns of Raj's team's work.

He switched back. But the episode left him with a lasting skepticism about benchmark claims and a determination to develop his own evaluation methodology.

The Testing Battery

Raj spent a weekend developing what he calls his "capability battery" — a set of representative test tasks that reflect his team's actual work and can be consistently applied to evaluate any new AI coding tool.

The six-task battery:

Task 1: The Simple Implementation A clearly specified function that any competent developer could write. The test here isn't difficulty — it's correctness and code quality. Raj evaluates: Does the output work? Is it readable? Does it follow language idioms? Is the implementation sensible?

His current tool gets this right virtually 100% of the time. He uses this as a baseline — any new tool that struggles with Task 1 gets eliminated immediately.

Task 2: The Edge Case Challenge A function that looks straightforward but has important edge cases: null handling, empty inputs, boundary values, large inputs that might cause performance issues. Raj evaluates whether the tool addresses edge cases without being prompted, and how well it handles them when it does.

His experience: most tools get the happy path right. The edge cases reveal quality differences that don't show up in standard benchmarks.

Task 3: The Hidden Bug Raj takes a function from his actual codebase (anonymized where needed) and introduces a specific bug — a type of error that experienced developers sometimes miss in review. He asks the AI to review the code and identify any issues.

This task tests something different from generation: reasoning about existing code with a specific problem. His experience: tools vary dramatically in this capability. Some catch his planted bug immediately; some miss it entirely; some identify it along with real issues in the code that he hadn't planted.

Task 4: The Code Explanation He gives AI a piece of his actual codebase — complex, non-trivial production code — and asks it to explain what it does, how it works, and what a developer modifying it would need to know.

This task tests whether the tool can reason about real-world code, not just toy problems. Evaluation criteria: Is the explanation accurate? Is it at the right level of detail? Does it identify the non-obvious aspects that would actually help a developer?

Task 5: The Refactoring Task He takes a piece of functional but suboptimal code (typically something his team has flagged in technical debt discussions) and asks AI to refactor it for readability, maintainability, or performance.

Evaluation: Does the refactored version actually work? Is it better in the specified dimension? Does it introduce new problems? Raj specifically looks for whether the tool understands the constraints — refactoring that makes code "better" by one metric while making it worse by another is a failure.

Task 6: The Security Check He presents code that handles user input, database queries, or authentication — areas with common security vulnerability patterns — and asks AI to identify any security issues.

This task is the most important to Raj's team because security failures in production are expensive and damaging. He evaluates: Does the tool identify the vulnerabilities he's aware of? Does it correctly reason about security tradeoffs? Does it avoid false alarms that would create alert fatigue in code review?

Running the Battery

Raj runs the full battery on any new tool that (a) has received substantive positive coverage from sources he trusts, (b) addresses a specific limitation in his current tooling that he's been aware of, or (c) one of his team members has found valuable and wants to propose for team adoption.

The battery takes approximately 90 minutes to run. He could do it faster, but he's learned to be deliberate: rushing through the evaluation produces the same result as not testing.

He documents each battery run in a simple shared document that his team can see. The documentation includes: - Tool name and version - Date of evaluation - Score on each task (1-5 scale with brief notes) - Overall assessment: adopt, evaluate further, decline - Any specific use cases where the tool performed notably better or worse

What the Battery Has Revealed

Over 18 months, Raj has run his battery on 11 different AI coding tools and 4 major updates to tools already in his stack.

The most consistent finding: the gap between benchmark performance and real-world performance varies dramatically across tools.

Some tools that score impressively on standard benchmarks underperform on his battery, particularly on the edge case and security tasks. Others that don't generate as much press attention perform well on his battery and have become part of his team's toolkit.

The specific patterns he's found:

Tools that perform well on standard generation often underperform on reasoning about existing code. Tasks 3, 4, and 5 — which require understanding and reasoning about code that already exists — are harder than Tasks 1 and 2, and the skill levels vary significantly.

Security awareness varies from negligible to excellent. Some tools routinely miss common vulnerability patterns; others catch them reliably. For Raj's team, this is a safety-critical distinction.

Explanation quality is often the best predictor of overall quality. The tools that explain code most accurately and usefully — Task 4 — tend to perform well across the battery. His hypothesis: the ability to reason about and articulate what code does is the underlying capability that drives good performance on the other tasks.

The Protocol's Value to His Team

Raj's testing protocol has become part of his team's culture. When a team member wants to propose a new tool, the first question is "Has it run the battery?" When a team member questions why a new tool that everyone's talking about isn't being adopted, Raj can explain its battery performance.

This has had two effects:

First: It has made tool decisions more rational and less reactive. The team doesn't chase every new tool that gets coverage; they evaluate the tools that seem worth evaluating, and they have a consistent methodology for doing so.

Second: It has improved the team's collective understanding of what makes AI coding tools actually useful. The battery conversations — "why did this tool fail Task 3?" "why is security performance so variable?" — have deepened the team's understanding of AI coding tool capabilities and limitations.

The Limits of the Protocol

Raj is clear about what his battery doesn't test:

Long-term reliability. The battery is a 90-minute assessment. It can't predict how a tool will perform over six months of daily use at scale. He's had tools that passed the battery but showed reliability problems in production use.

Team fit. The battery tests capability, not usability. A technically excellent tool with a poor developer experience can underperform a slightly less capable tool that integrates smoothly into the team's workflow.

Evolution. A tool that scores poorly in one battery run may perform very differently in a major update three months later. The battery is a snapshot, not a prediction.

For these reasons, Raj treats battery results as a necessary but not sufficient condition for adoption. A tool must pass the battery, but a passing score doesn't guarantee adoption — it earns the right to a pilot.

The Broader Principle

Raj's testing protocol embodies a principle that extends beyond AI tool evaluation: first-hand assessment is irreplaceable, and the right assessment tests the things that actually matter to your specific work.

Generic benchmarks test generic tasks. Marketing materials test best-case scenarios. Even trusted colleagues' recommendations reflect their specific use cases, which may differ from yours. The only assessment that directly answers "will this tool improve my team's specific work?" is the assessment you design and run yourself.

This is a time investment. Raj acknowledges that 90 minutes per tool evaluation, multiplied across 11 tools over 18 months, represents a meaningful commitment. But he also calculates the cost of the alternative: adopting the wrong tool, dealing with quality problems that the battery would have caught, switching costs when a tool underperforms in production.

By his estimate, the testing protocol has saved his team significantly more time than it has cost — primarily by preventing two or three tool adoptions that would have caused production quality problems.

The battery is also compounding: with each run, Raj understands the capability landscape better. The 11th evaluation took less time and produced better insight than the first. The protocol itself has become a skill.