RAG Explained

Here are 10 RAG architectures every AI builder should know — ranked from simplest to most powerful:
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1️⃣ Simple RAG
User asks → system retrieves chunks → LLM answers.
The "Hello World" of RAG. Great for demos. Rarely enough for production.
2️⃣ RAG with Memory
Same as above, but the system actually remembers your previous messages.
One layer added. Huge difference in user experience.
3️⃣ Branched RAG
Complex question? Break it into sub-questions. Run parallel retrieval for each. Synthesize into one answer.
Built for questions that are really 3 questions in disguise.
4️⃣ HyDE (Hypothetical Document Encoding)
Problem: a question and its answer look very different in embedding space.
Fix: ask the LLM to generate a hypothetical answer first, then use that as the search vector.
Retrieval quality improves significantly.
5️⃣ Adaptive RAG
Not every question needs retrieval. A routing layer decides: skip retrieval, do simple retrieval, or trigger complex multi-step retrieval.
Smarter. Cheaper. More efficient at scale.
6️⃣ Corrective RAG (CRAG)
Adds a quality gate after retrieval.
If retrieved docs are low-quality → reformulate the query or fall back to web search.
Your pipeline stops confidently using bad context.
7️⃣ Self RAG
The LLM critiques its own answer while writing it.
Reflection tokens ask: "Is this supported by the retrieved context?"
More accurate. More honest. More transparent.
8️⃣ Agentic RAG
No fixed pipeline. The LLM decides its own next steps.
Search → evaluate → call an API → search again → answer.
It loops until the answer is actually good enough.
9️⃣ Multimodal RAG
Your documents have charts, tables, and images. Standard RAG ignores them. Multimodal RAG doesn't.
Vision models describe visuals so they can be retrieved like text.
🔟 Graph RAG
Treats your documents as a knowledge graph, not a flat pile of chunks.
Maps entities and relationships explicitly.
Crushes vector search when the answer requires connecting multiple facts.
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Which one are you using right now? Drop it in the comments 👇

Видео RAG Explained канала Gaurav Dhote