RAG Analysis

Two source passages support an illustrative answer with a section and page reference that can be checked. Illustration: keep an explanation connected to the passages used to write it.
projects

RAG Analysis answers a question using passages found in my textbooks and course notes, with references back to the material it used. That lets me inspect an explanation and continue reading at its source. The reference is a starting point for checking the answer, not a guarantee that the model interpreted it correctly.

One useful choice is limiting how many passages a single file can contribute. Otherwise, a relevant textbook can fill the available space and crowd out lecture notes or another author’s explanation. Follow-up questions are also rewritten using the recent conversation, so “explain that differently” becomes a specific question the search tools can use.

A clearly illustrative example, rather than a quotation from a recorded answer:

Question: What does the spectral theorem help explain about a real symmetric matrix?

Answer sketch: It gives perpendicular directions along which the transformation acts by scaling, making its action easier to describe.

Reference format: Section 7.4, pages 264–266.

The name RAG means retrieval-augmented generation: find relevant passages first, then supply them to the model writing the answer. This project reuses the Source Indexer for that search. An August 2026 trial answered a spectral-theorem question using two source files; a follow-up exchange also exercised the conversation-based search step. Those were specific trials, not a broad evaluation of answer accuracy.

What is remembered

The running chat retains its conversation, but restarting the process does not restore the full dialogue. With a course selected, the tool can save answer, hint, draft-feedback, and verification events. Recent recorded learning gaps can inform later answers, and a session recap can turn the recorded discussion into a study note.

Requesting a report also saves the answer as a note with its source references. Those references identify the passages in the Source Indexer, so Summary Enhancement can reopen that material and develop a short answer into fuller explanations and examples. The Study Guide Pipeline offers another starting point when I already have a topic list. Saved notes and logs survive separately from the live chat history.

A companion resolver answers question markers in handwritten notes. Its recorded seven-question run included four source-grounded answers and three marked as ungrounded. General answers remain leads to check, and retrieving a handful of passages is too narrow to establish comprehensive coverage of a week’s study.

The README describes the current interfaces. Tutor Diagnosis adds feedback on an attempt; the separate Socratic Tutor manages an ongoing tutoring session.

Aaron Scherf
Authors
Aaron Scherf (they/them)
PhD Student
I am a PhD student in Sustainable Development at Columbia University and a former USAID Foreign Service Officer. In Ukraine, I led the monitoring and evaluation team for over 50 development projects during the war. Previously, I worked on program design and social inclusion in Colombia, as well as budget analysis and knowledge management in Washington. Those experiences taught me how to ensure evidence is trustworthy and actionable, especially in conditions of rapidly changing and incomplete information. I now bring that perspective to research, data analysis, and practical software tools. I am available part-time for consulting and applied research collaborations.