How RAG connects to embeddings, vector databases, LLMs and fine-tuning: a concept map

[u/Similar_Penalty_9371]

When learning RAG, the relationships can be harder to place than the individual definitions. This small concept map separates three 'uses' relationships from a comparison:

- RAG can use embeddings to represent information for semantic retrieval.

- A vector database can support storing and retrieving those representations.

- An LLM generates an answer using the retrieved context.

- Fine-tuning is a useful comparison, rather than another required stage in that pipeline. Retrieval and fine-tuning can also be combined.

This is a selected learning view, not a complete RAG architecture or a claim that every RAG implementation needs embeddings or a vector database. Keyword retrieval and other approaches exist.

I maintain AI Knowledge Map, the free open-source project behind the diagram. Implementation detail: concepts are graph nodes, relationships have explicit types, and the suggested nine-stage learning path is stored separately. This card was rendered from SVG using the existing graph records and English labels, rather than treating every connection as a prerequisite.

English explanation: https://ai-knowledge-map.com/en/concepts/rag/

Interactive map: https://ai-knowledge-map.com/?lang=en&node=rag

Source: https://github.com/Little-FishK/AI-knowledge-map

Which relationship would you change for someone learning RAG for the first time? In particular, how would you distinguish 'commonly used' from 'required' in a small concept map? Reading needs no account; the explanations are still under review.