  This paper presents the use of Retrieval Augmented Generation (RAG) to
improve the feedback generated by Large Language Models for programming tasks.
For this purpose, corresponding lecture recordings were transcribed and made
available to the Large Language Model GPT-4 as external knowledge source
together with timestamps as metainformation by using RAG. The purpose of this
is to prevent hallucinations and to enforce the use of the technical terms and
phrases from the lecture. In an exercise platform developed to solve
programming problems for an introductory programming lecture, students can
request feedback on their solutions generated by GPT-4. For this task GPT-4
receives the students' code solution, the compiler output, the result of unit
tests and the relevant passages from the lecture notes available through the
use of RAG as additional context. The feedback generated by GPT-4 should guide
students to solve problems independently and link to the lecture content, using
the time stamps of the transcript as meta-information. In this way, the
corresponding lecture videos can be viewed immediately at the corresponding
positions. For the evaluation, students worked with the tool in a workshop and
decided for each feedback whether it should be extended by RAG or not. First
results based on a questionnaire and the collected usage data show that the use
of RAG can improve feedback generation and is preferred by students in some
situations. Due to the slower speed of feedback generation, the benefits are
situation dependent.
