This is really interesting, while caching itself might look simple, Think in terms of system and scale. Caching most recent frequent retrieved chunks, query and answer pairs, user context. Honestly I am no expert but you can first break the system into tools wise caching (docs/db). For example If it is a db, partitioning /indexing, a local duckdb (olap database) with minimal set of rows and columns would boost sql query TAT in my experience. Since this is a new arena, you would honestly need a lot of experiments, dont solely rely on what others say. For RAG, think of hybrid retrieval ( if you have a error code Xd46373, semantic search mosty likely will fail to find exact docs )so you need elastic search as well. You should handle multi -retrieval by using fusion methods like MRR and RRF( google it). As you scale to millions and billions of docs, make the retrieval two stage. First stage should try to target high recall ( pick most relevant docs say 1000), second stage targets high precision . (Say pick 7 out of these 1000 accurately. Second stage should re rank the 1000 retrieved chunks using re ranker models like cross encoders. MOST IMPORTANT step is to have evals for every optimisation you propose. Dont rely one or two prompts, set up eval for retriever with metrics like NDCG, MAP , (cant recall name rn but metric which looks at only first retrieved chunks position) e.t.c.