The readme on Github does say the system produces hallucinated sources https://github.com/anonnerdfrenzy/survival-llm#how-the-model..., but really you're correct, for this kind of highly targeted use case the LLM's summary could probably be ignored entirely and the system could work extremely well even just as a search engine: index all of the content up front, chunked by paragraph and maybe enriched with a HyDE step, have the LLM facilitate running a handful of hybrid keyword/semantic searches based on the user's question and reranking the results, and show the exact pieces of text in the corpus that are most responsive to their question. No summary needed.
Query expansion and reranking are both valuable and things that even small LLMs are pretty good at. People are broadly not great at writing search queries, so a system that takes a poorly phrased question, turns it into several different actual search queries and then merges and sorts the results by relevance can have a big impact. All the better if the AI can ask the user some clarifying questions.
