Why RAG Fails
Bad chunks, weak retrieval, ignored context, hallucinated citations. Five common failure modes.
A free, animated why rag fails you can read here or embed on any website, from Scrollchart.
Five RAG failures: chunking that splits relevant context, retrieval that misses the right doc, retrieval that returns the right doc but it gets buried by context, the LLM ignoring retrieved context, and the LLM hallucinating a citation. Each annotated with the eval signal that catches it.
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Source & accuracy
This why rag fails is an editorial illustration built to represent the concept accurately. Where it shows figures, they are typical or representative values chosen to make the relationship clear, not a single underlying dataset. The diagram and its explainer are reviewed and maintained centrally, and updated over time as understanding improves.
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Other ai & machine learning explainers
Forward Pass: Inputs to Logits
Activations flow layer by layer. Linear, then nonlinearity, then linear again, ending in logits.
Backpropagation: Gradients Flow Backward
Loss at the end. Gradients propagate backward via the chain rule, updating each weight.
Activation Functions Compared
ReLU, sigmoid, tanh, GELU, SiLU. Curves, gradients, and where each shines.