AI Coding Dataset Generation for Model Training

Problem:

A client developing a large language model (LLM) needed high-quality, multilingual coding data to improve performance across specific benchmarks such as HumanEval, CodeForces, BigCodeBench, and LiveCodeBench. The client was experiencing inconsistent model performance when generating, fixing, or explaining code in multiple languages.

Solution:

I curated a custom dataset of prompts and gold-standard responses for tasks like code generation, code completion, bug fixing, test case creation, and code explanation across 7 programming languages (Python, JavaScript, TypeScript, Java, PHP, Ruby, SQL). Each prompt was crafted or iteratively refined to expose failure points in the client model, and corrected responses were provided along with runnable and testable scripts.

Expected Impact:

The curated data was intended to fine-tune and align the client’s model to industry-grade benchmarks with a focus on correctness, reproducibility, and multilingual code understanding. The project increased prompt diversity, edge case coverage, and output validity across code-based tasks.

Steps Taken to Curate the Project:

Reviewed client benchmarks and evaluated performance gaps via model-in-the-loop testing.
Iteratively crafted prompts that triggered model failures in code generation or completion.
Provided corrected "gold standard" responses with explanations, edge cases, and runtime-safe code.
Ensured code quality by aligning with style guides (e.g., PEP8), using modern libraries, and ensuring proper input/output formatting.
Generated test cases and evaluation scripts for automated verification of solutions.
Ensured compliance with client formatting, security, and documentation standards.

Skills Used:

Prompt engineering, LLM evaluation, Python, JavaScript, SQL, test-driven development, code documentation, error pattern analysis, model debugging, technical writing, Git, reproducibility validation