Yes, obviously useful for prototyping and creating hype articles & tweets with fun examples. However any engineer is capable of doing their own rag with the same effort (minimal data extraction using the ancient pdf/scrape tools that are still open sota, or use cloud ocr for best —-> brute force chunking —-> embed —-> load in Ann with complementary metadata store)
Anyone doing prod needs to know the intricacies and make advanced engineering decisions. There’s a reason there aren’t similar end-to-end abstractions over creating Lucene (solr/elastic) indexes. Hmm, why not after many decades? …
In reality, the RAG tech is not entirely novel— it’s etl. Which in reality, complex etl is often a serious data curation effort. LLMs are the closest thing to enabling better data curation, and as long as you aren’t competing with open ai (arguably any commercial system is) then you can use chatgpt to create your chunks.
Beyond this embedding strategies are nice to abstract but the best approach to embeddings still remains to create your own and figure out contextual integration on your own. Creating your own can also just be fine-tuning. Inference is often an ensemble depending on your use case.