Designing RAG Thesis Framework

I’m developing a research thesis that compares Retrieval-Augmented Generation (RAG) systems through a behavioural data-science lens, and I’d like a collaborator to help me shape the theoretical framework from the ground up.

What I need now is structured brainstorming that turns my initial ideas into a solid, defensible design: clarifying the central research questions, mapping the relevant literature on accuracy, performance, cost, scalability, security, and privacy, and selecting evaluation metrics that make sense for behavioural data science. I’ll be working with Python-based tooling the usual OpenAI endpoints—so experience with these environments will make our discussion far more concrete.

Deliverables I’m looking for:
• A concise framework outline (problem statement, hypotheses, variables, proposed methodology)
• An annotated reading list highlighting the most influential papers on RAG and behavioural data science intersections
• A comparison matrix that shows how alternative system designs could be evaluated against the selected criteria

Once these three artifacts are in place, I’ll take over the empirical work myself. If you enjoy deep theoretical thinking, can reference cutting-edge RAG research with ease, and like turning abstract ideas into well-structured plans, let’s talk.