Naive, multimodal, HyDE, corrective, graph, hybrid, adaptive, and agentic patterns—at a glance.
Once you've decided that RAG is the right approach, the next step is choosing the right RAG architecture for your use case. 8 RAG architectures We prepared the following visual that details 8 types of RAG architectures used in AI systems: Let’s discuss them briefly:
1) Naive RAG
Retrieves documents purely based on vector similarity between the query embedding and stored embeddings. Works best for simple, fact-based queries where direct semantic matching suffices.
2) Multimodal RAG
Handles multiple data types (text, images, audio, etc.) by embedding and retrieving across modalities. Ideal for cross-modal retrieval tasks like answering a text query with both text and image context.
3) HyDE
Queries are not semantically similar to documents. This technique generates a hypothetical answer document from the query before retrieval. Uses this generated document’s embedding to find more relevant real documents.
4) Corrective RAG
Validates retrieved results by comparing them against trusted sources (e.g., web search). Ensures up-to-date and accurate information, filtering or correcting retrieved content before passing to the LLM.
5) Graph RAG
Converts retrieved content into a knowledge graph to capture relationships and entities. Enhances reasoning by providing structured context alongside raw text to the LLM.