Research areas where experiments become practical guidance for AI adoption, evaluation, and governance.
Evaluating large language models, prompt patterns, and practical adoption paths for modern AI capabilities.
Researching prompt injection attacks, guardrails, moderation systems, and risk controls for AI systems that need to fail gracefully.
Designing retrieval-augmented generation patterns, embedding strategies, and decision frameworks for effective knowledge retrieval.
Studying how to assess AI opportunities, evaluate systems, build reproducible pipelines, and measure what actually matters.
Building human-agent collaboration platforms like Prax + TeamWork — shared workspaces where humans and AI agents work side-by-side with the same tools, memory, and desktop.
Researching sparse autoencoders and interpretability techniques to understand and steer model behavior at the feature level.
An independent AI research and advisory studio for applied AI systems.
Documenting experiments, discoveries, and lessons learned across the rapidly evolving AI landscape.
Building real applications to understand how AI technologies work in practice and what credible adoption requires.
Translating research findings into practical guidance for AI evaluation, architecture, safety, and implementation strategy.
Questions about AI evaluation, RAG, agent systems, or responsible adoption? Reach out.
PraxAgent LLC is an independent AI research and advisory studio. Public work here is educational, experimental, and independent.