High-Precision Evaluation of Multi-Language Code Generation

Introduction

A global technology company engaged our team to conduct high-accuracy evaluations of AI-generated code responses across 25 programming languages. With the goal of improving large language model (LLM) performance through Reinforcement Learning from Human Feedback (RLHF), the project required consistent, expert-level assessments of code outputs under strict time constraints.

About 300 evaluation tasks were completed in just three weeks, each involving hands-on code testing, comparative analysis, and structured feedback using annotation protocols designed for scalable LLM training.

The Challenge

● Multiple coding language complexity: Tasks spanned 25 programming languages, each requiring adaptable evaluation techniques.
● Subjective judgment: Responses demanded nuanced assessments of instruction adherence, code logic, and problem-solving quality.
● Compressed timeline: The full scope of work had to be delivered within three weeks.
● Structured feedback needs: Feedback needed to follow predefined issue categories with subjective explanations, and include screen recordings as proof of work at every step to demonstrate how the workflow was conducted.

The Solution

To address these challenges, a structured and repeatable evaluation process was implemented, combining both technical testing and qualitative review:

● Prompt-level analysis: Each task required reviewing multiple AI-generated responses for accuracy, completeness, clarity, and instruction alignment.
● Hands-on code validation: Every single line of code generated by the LLM was individually examined and tested for correctness. Entire codebases were created to replicate the issue being described in the prompt wherever needed, so that the solution provided could be thoroughly evaluated.
● Issue categorization and annotation: Deviations were flagged using a structured taxonomy to support training workflows.
● Comparative response assessment: Evaluators ranked responses using the client’s platform tools supported by written justifications.

The Result

Nearly 300 programming tasks were evaluated across 25 languages, with each response reviewed in detail and supported by evidence-based reasoning. This approach yielded a 90% accuracy among many other significant outcomes:
● High-quality training data: Evaluations included not just issue labels, but clear explanations and proof of work that enhanced the depth and usability of training data.
● Accelerated model iteration: The structured methodology enabled faster learning cycles and better-informed model refinement.
● Improved reliability: Subtle logic and execution issues, often missed by automated tools, were consistently surfaced through constant explicit examination by the team at every step.
● On-time and budget delivery: The entire project was completed within the three-week timeline while staying within the predefined AHT (Average Handling Time) of 2 hours, supporting the client’s training and deployment schedules.

This project demonstrated the value of combining hands-on testing with structured human feedback for scalable, high-accuracy LLM training in real-world coding environments.