Turing executed a two-pronged strategy to enhance LLM response accuracy and rapidly scale the training team. A bespoke vetting process sourced and integrated 130+ LLM trainers in under two months through rigorous trial runs. A top-down communication strategy kept all team members aligned with frequent task instruction updates, while a dedicated team maintained quality standards through continuous feedback and coaching.

Turing implemented six targeted evaluation projects over two weeks to systematically analyze a leading tech company's custom LLM strengths and weaknesses. The evaluation covered Guided API Evaluation, Freestyle API Evaluation, Prompt Breaking, LLM and Human Benchmark Analyses, Community Findings Aggregation, and RLHF & Calibration. Four assessment levels tested tasks from principal engineer-level to rudimentary-level complexity.

Turing designed a large-scale software engineering benchmark from a complex open-source codebase. Each task includes a self-contained prompt from a real issue report and a solution-agnostic E2E UI test grader that accepts any valid solution and rejects invalid ones. Unlike unit-test-only grading, E2E tests reflect complete user workflows, are solution-agnostic by design, and are harder to game.

Turing delivered a multimodal dataset combining real-world code edits, visual question-answering (VQA), and structural sketches derived from website screenshots. The three-pronged annotation pipeline included code edit tasks (HTML/CSS/JS modifications at multiple difficulty levels), web sketches (hand-drawn layouts with standardized component tagging), and VQA (five high-quality questions per screenshot spanning functional, complex reasoning, and general understanding). Each task underwent two-step human validation for quality assurance.

Turing designed a large-scale software engineering benchmark from a complex open-source codebase. Each task includes a self-contained prompt from a real issue report and a solution-agnostic E2E UI test grader that accepts any valid solution and rejects invalid ones. Unlike unit-test-only grading, E2E tests reflect complete user workflows, are solution-agnostic by design, and are harder to game.