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Although RAG is often implemented via vector databases to find 'relevant' content, I'm not sure that's a necessary component. I've been doing what I call RAG by finding 'relevant' content for the current prompt context via a number of different algorithms that don't use vectors.

Would you define RAG only as 'prompt optimisation that involves embeddings'?

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Sure thing, your RAG approach sounds intriguing, especially since you're sidestepping vector databases. But doesn't the input context length cap affect it? (chatgpt plus at 32K [0] or gpt4 via open ai at 128K [1]) Seems like those cases would be pretty rare though.

[0]: https://openai.com/chatgpt/pricing#:~:text=8K-,32K,-32K

[1]: https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turb...

Yes, context window is a limiting factor, but that's true however you identify the content to augment generation.

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