AI Dataset Engineering
Building better datasets through synthetic instruction generation, negative examples, validation and cross-model evaluation.
Explore Dataset Engineering →
Featured Research
AI Dataset Engineering
Better Data. Better Models.
Designing, generating and evaluating high-quality datasets for Large Language Models. My research focuses on synthetic data generation, dataset validation and systematic evaluation across multiple model families.
20
Model Variants
1,940
Benchmark Tasks
Cross-Model
Validation
Current focus
Research and development
From dataset engineering and document collections to retrieval pipelines, specialized language models and transparent evaluation.
Local knowledge systems
Privacy-conscious assistants retrieving evidence from organisational knowledge bases with transparent citations.
Explore Local RAG →Task-specific fine-tuning
Improving open-weight language models for grounded answers, structured output and robust domain adaptation.
Explore Fine-Tuning →Evaluation & Benchmarks
Measuring quality with reproducible benchmarks, citation checks and cross-model comparisons.
Explore Evaluation →Open work
Models, code and documentation
Public model releases, repositories and technical documentation make experiments inspectable and reusable.
Selected work
Projects
Applied research connecting technical development, university practice and open knowledge.
RAG in Teaching
Local AI-supported FAQ systems for teaching, student services and university administration.
Project details →RAG Memory Bot
An experimental memory and retrieval assistant that connects conversational interaction with controlled document retrieval.
Project details →LaTeX Resources
More than two decades of courses, examples, package documentation and material for scientific writing.
Browse LaTeX resources →