AI PROJECT
EXECUTION
Engineering a Repeatable LLM Evaluation Platform
Project Context
An AI company developing large language models needed a consistent way to evaluate coding, reasoning, multilingual tasks and structured outputs. Different evaluation methods made defects harder to classify and model versions difficult to compare.
Execution Objective
Design and implement a standardized evaluation workflow that creates comparable evidence of model quality across test domains and model releases.
How the Project Is Executed
How the Project Is Executed
01 — Define Evaluation Scope
Agree evaluation domains, quality criteria, scoring rules and acceptance thresholds with stakeholders.
02 — Build the Test Foundation
Create curated prompts, expected responses, edge cases and domain-specific datasets; document annotation instructions.
03 — Implement Evaluation Pipelines
Run model outputs through checks for correctness, instruction adherence, reasoning quality and structured-output validity.
04 — Add Specialist and Human Review
Evaluate coding, Python, JSON and required multilingual cases; apply reviewer quality checks and error classification.
05 — Automate Regression Testing
Compare model versions against a consistent test suite and flag changes in quality or recurring failure patterns.
06 — Report and Iterate
Publish scorecards and benchmark reports, review defects with stakeholders and refine the test suite.
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Project Deliverables
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Curated training and evaluation datasets
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Annotation guidelines and quality-control procedures
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Evaluation pipeline and automated regression test scripts
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Model scorecards, benchmark reports and defect taxonomy
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Evaluation documentation and review workflow
Expected Business Impact
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More consistent comparison between model versions
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Earlier visibility into output defects and failure patterns
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A repeatable quality-assurance process to support model iteration
Success Measures: Evaluation accuracy; reviewer/annotation agreement; defect detection rate; regression pass rate; cost per evaluated sample.
Relevant Joy IT Capabilities: LLM evaluation specialists, Python/Machine Learning Engineering, LLM QA and AI Quality Analysts.