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Apertus Eval Prep is a reproducible framework for studying how evaluation configurations affect LLM benchmark results and model-ranking stability.
The project systematically evaluates factors such as prompt formulation, chat templates, inference backends, quantization, decoding settings, hardware, safety, hallucination, robustness, and evaluation cost. It uses controlled one-factor-at-a-time experiments to isolate the effect of individual configuration changes.
The framework includes experiment configuration, result registries, reproducible artifacts, statistical analysis, automated validation, and research-report generation. It also incorporates confidence intervals, paired statistical testing, Kendall's tau for ranking stability, and cost-aware analysis.
The current repository contains 23/34 completed experimental cells and 58 passing automated tests.
This project demonstrates my ability to build reliable AI evaluation infrastructure rather than relying only on subjective model comparisons.
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RAG or fine-tuning? The question I get asked most and the answer is almost never "fine-tune."
Quick way to think about it:
→ Your data changes often, or you need the model citing real, current info? Go RAG. Faster to build, cheaper to update, and you're not retraining every time something changes.
→ You need the model to consistently think or respond in a very specific style/format that plain prompting won't hold onto? That's when fine-tuning earns its cost.
Most business use cases internal knowledge bots, support agents, research assistants are RAG problems wearing a fine-tuning costume. People reach for the expensive option because it sounds more "AI," not because it's the right one.
Building something and not sure which one fits? Happy to help you think it through.
#AI #MachineLearning #RAG #SoftwareDevelopment #Contra
