Yeah I would recommend sticking to RAG on naively chunked data for weekend projects by 1 person. Likewise, a consumer tool like perplexity's search engine where you minimize spend per user task or go bankrupt, same thing, do the cheap thing and move on, good enough

Once RAG projects become important and good answers matter - we work with governments, manufacturers, banks, cyber teams, etc - working through data quality, data representation, & retrieval quality helps

Note that we didn't start here: We began with naive RAG, then relevancy filtering, then agentic & neurosymbolic querying, then dynamic example prompt injection, and now are getting into cleaning up the database/kg itself

For folks doing investigative/analytics projects in this space, happy to chat about what we are doing w Louie.AI. These are more implementation details we don't normally write about.