Evaluating large language models on coding tasks requires not only functional correctness, but also alignment to developer intent, specification handling, and real-world constraints. The client needed:

• Tasks that broke the model without being adversarial or trivial
• Coverage across various programming languages and prompt types
• Clear annotations of failure cause such as factual error versus instruction-following gap
• Ground truth responses to support training or fine-tuning downstream

Turing’s team of prompt engineers and coding trainers collaborated to build more than 400 high-integrity tasks using our coding guidelines. Each task followed a fixed annotation pipeline:

Metadata and prompt design

Internal teams generated the metadata schema for both single-turn and multi-turn tasks. Trainers then authored model-breaking prompts, ensuring:

• Clarity and realism in the user request
• Reasonable difficulty without ambiguity
• Language diversity and representation of common developer workflows

Evaluation and ideal response generation

Each prompt was run against the client’s model. Trainers evaluated model outputs, tagged failure types, and wrote:

• Ideal responses that correctly fulfilled the prompt
• Turn-level breakdowns in multi-turn samples to isolate where model failure occurred
• Instruction-following annotations where the model ignored structural constraints or misunderstood test cases

Coverage across languages and task types

Prompts covered categories such as code generation, code completion, fixing, test-case authoring, explanation, and output prediction. Tasks emphasized:

• Multistep reasoning and constraint handling
• Doctest versus descriptive test case recognition
• Return type inference and prompt clarity analysis

All samples were grounded in real-world software engineering use cases.

• Delivered more than 400 model-breaking coding tasks built for fine-tuning, benchmarking, or instruction-following evaluation
• Covered more than 10 programming languages, supporting multilingual model benchmarking
• Defined and tagged four distinct failure types to support targeted error analysis
• Maintained prompt  and code separation to reduce bias in ideal response generation
• Ensured every ideal solution was verified for syntactic and functional correctness

Turing's model-breaking dataset provides:

• High-signal QA samples for model training, tuning, and evaluation
• Realistic developer-style prompts and failure typologies
• Fine-grained insights into how and why models fail on specific coding tasks

With full coverage across prompt types, languages, and failure dimensions, the dataset supports both research and production alignment goals.