TLDR

A foundation model company needed to build a scalable, automated evaluation program for code generation across multiple models.

Revelo delivered 1,000+ expert-evaluated tasks in 8 days, creating task-specific rubrics that transformed subjective quality dimensions into verifiable criteria—enabling ongoing automated benchmarking across 7 models and 100+ programming languages.

THE CHALLENGE

Building rigorous evals at scale across an impossibly broad technical landscape

The client faced a complex challenge: create a continuous evaluation program that could assess code generation quality across competing models. But this wasn't just about correctness—they needed to measure subjective dimensions like clarity, scalability, and code design.

Broad Skill Requirements: The evaluation needed coverage across 40+ programming languages, 160+ frameworks, and over 100 knowledge subdomains—from mobile development to machine learning.

Multi-Model Complexity: Each prompt required evaluation across 7 different models, including experimental versions, demanding consistent scoring despite varying output styles.

Automation Imperative: While human expertise was essential for nuanced evaluation, the rubrics needed to be "automatable"—creating verifiable criteria that models could eventually self-score.

Quality at Velocity: The client needed both depth (thoughtful, expert-level evaluation) and speed (rapid scaling to production volumes).

OUR APPROACH

Where code expertise meets evaluation methodology design

Revelo recognized this required more than just supplying evaluators—it demanded co-designing the entire evaluation framework:

Taxonomy Development: We helped build the knowledge domain structure, ensuring comprehensive coverage across software engineering disciplines.

Rubric Engineering: We created task-specific rubrics that translated subjective qualities into measurable, verifiable criteria.

Expert Curation: We leveraged our 400,000+ developer network to find specialists in niche areas—from Qiskit quantum computing to legacy COBOL systems.

Prompt Complexity Calibration: We trained annotators to create genuinely challenging prompts that would maximize differentiation between models.

THE SOLUTION

A sophisticated evaluation engine powered by expert engineers

Working as true partners, we delivered a comprehensive evaluation system:

Task-Specific Rubric Creation:

• Each prompt came with custom evaluation criteria
• Subjective dimensions (clarity, scalability) broken into verifiable checkpoints
• Weighted scoring aligned with real-world importance

Example Rubric Structure:

• Correctness: Syntax validity, proper table names (weight: 5)
• Instruction Following: Correct sorting, filtering logic (weight: 5)
• Scalability: Efficient window functions used (weight: 3)
• Clarity: Well-structured CTEs, clear naming (weight: 1)

Sophisticated Tooling Integration:

• Multi-model comparison in single interface
• Model-blind review options for unbiased scoring
• Real-time calibration across evaluator pool

Quality Assurance Layers:

• Multi-step QA process with second-pass validation
• Continuous calibration sessions
• Performance-based incentive alignment

THE RESULTS

From zero to comprehensive benchmarking in record time

The impact was immediate and substantial:

Velocity Achievements:

• 0 to 100% completion in 8 days across 25 parallel queues
• 1,000+ high-quality evaluated tasks delivered
• 91 JavaScript specialists activated in the highest-volume queue
• Sustained quality despite aggressive timeline

Long-Term Value Created:

• Automated evaluation capability through verifiable rubrics
• Continuous benchmarking system for ongoing model comparison
• Loss-bucket analysis revealing model strengths/weaknesses:
  • Code Quality/Design: 4-38% loss rates across models
  • Scalability: 8-21% loss rates
  • Clarity: 13-46% loss rates

Strategic Outcomes: The client gained not just data, but a repeatable evaluation methodology. The automated rubrics now enable continuous assessment of new model versions, while the loss-bucket analysis guides targeted improvements.

What began as a one-time evaluation became an evergreen benchmarking system—positioning the client to maintain competitive advantage as models rapidly evolve.