Building a coding assistant is already a challenge, but knowing whether a change actually makes it better is an entirely different beast. The naive approach? Ask the assistant to generate code using the same prompt before and after the change, then manually inspect the results. That might work for something simple—a JavaScript calculator with embedded CSS—but as soon as we scale up to complex projects, this becomes impractical.

I started digging into existing solutions and quickly ran into HumanEval and Codex. While they aim to measure LLM coding performance, they don’t quite fit the need. HumanEval provides a set of Python function-generation tasks with unit tests, which is useful but limited. Codex evaluations rely on manually crafted benchmarks, which, again, require too much human intervention when testing incremental improvements in a live system. They also are more about measuring the code generation abilities of an LLM than the tooling on top that builds applications.

A Structured Approach: Unified Prompts and Automated Evaluation

What I think might work instead is a two-part system:

  1. Standardized Prompt Set for Code Generation: This would use the coding assistant to generate code for predefined prompts that reflect real-world use cases. The assistant’s output could then be compared across versions.
  2. Standardized Set of Test Cases: A list of LLM prompts that measure how well the project implements different aspects of the ask.
  3. LLM-Based Automated Evaluation: A secondary LLM would analyze the generated code and return either a numeric score or a list of passing test cases.

This approach eliminates the need for constant human intervention while still allowing for meaningful assessment of improvements. Instead of manually checking each output, an evaluation layer could determine if the new results are more syntactically correct, adhere to best practices, and—most importantly—function as intended.

Leveraging Existing Code Quality Tools

Another useful addition to the evaluation process could be integrating existing code quality testing tools such as SonarQube. These tools can analyze code for maintainability, security vulnerabilities, and other quality metrics, contributing valuable insights to the overall evaluation score.

Similarly, linting tools can provide additional feedback by flagging syntax errors, enforcing best practices, and ensuring code style consistency.

By incorporating these tools into the evaluation pipeline, we can create a more comprehensive and automated assessment of the coding assistant’s performance.

Challenges in Building Such a Tool

Of course, there are hurdles to overcome:

  • Designing Meaningful Prompts: The prompts need to represent a broad range of real-world scenarios, from simple scripts to complex architectures.
  • Automating Code Validation: The LLM evaluator needs a robust way to check correctness—maybe by running test cases, comparing logic structures, or checking against best practices.
  • Avoiding False Positives/Negatives: If the evaluation model is too lenient, bad code might pass. If it’s too strict, functional but unconventional solutions might get penalized.

The Next Steps

Someone needs to build this tool. Whether it’s a small, dedicated evaluation framework for a specific coding assistant or a broader service that others can use, having an automated way to track performance improvements is essential. This isn’t just about making life easier—it’s about making progress measurable.

If you’ve thought about this problem or have ideas on implementation, I’d love to hear your thoughts. Maybe together, we can figure out how to make coding assistants smarter, one evaluation at a time.