Everyone Is Using RAG Wrong

RAG became the default AI architecture before people even understood when to use it.

Here’s the problem:
Most AI apps don’t actually need retrieval systems.

If your entire knowledge base fits inside the model’s context window, you can skip vector databases completely and inject the data directly into the prompt.

Example:
A startup building an HR chatbot with 25 pages of company policy usually doesn’t need embeddings, chunking, retrieval pipelines, and reranking. The model can read the whole thing directly.

RAG makes sense when:
- your data changes frequently
- your knowledge base becomes very large
- your product needs citations or external retrieval

Otherwise, you may just be adding cost and latency.

Comment "RAG" and I’ll send you the full video explaining how RAG actually works.

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#RAG #VectorSearch #LLMArchitecture #PromptEngineering #AIInfrastructure #Embeddings #RetrievalAugmentedGeneration

Видео Everyone Is Using RAG Wrong канала Ganesh Ghatti AI