I’m Carles Rodríguez, a Senior Quality Engineer focused on AI systems—especially LLM evaluation and quality assurance for chat, search, and RAG workflows. I’ve built and applied reliable LLM-as-a-Judge evaluation frameworks, including gold standards, rubrics, and scoring criteria designed for repeatability and consistent decision-making across distributed teams.
I’ve worked on hallucination detection, retrieval grounding validation, instruction-following checks, and multilingual quality review (Spanish/Catalan/English). I enjoy turning evaluation findings into actionable improvements—through calibration, inter-rater consistency work, structured QA procedures, and detailed failure-mode analysis—while supporting AI alignment and safety quality in real-world production pipelines.
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This repository documents my practical experience in the evaluation, alignment, and qualitative analysis of large language models (LLMs) and speech systems.
The work presented here focuses on applied evaluation rather than model training, covering tasks related to alignment, reasoning quality, linguistic robustness, and real-world language behavior.
All materials are synthetic and designed exclusively for professional portfolio purposes.
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