RAG Limitations Overview
Retrieval-Augmented Generation is powerful, but it is not magic. This interactive infographic groups the most common RAG failures into three categories - context length limits, hallucination, and factual accuracy - and pairs each with a concrete example and a set of mitigation strategies. It closes by pointing to GraphRAG for the cases where standard RAG simply cannot keep up.
Interactive Demo
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Overview
Each of the three cards represents a category of limitation:
- Context Length Limits (blue): When the relevant material is larger than the model's context window, you must drop documents and risk losing critical detail.
- Hallucination (red): The model can generate fluent, confident text that is not supported by - or even contradicts - the retrieved context.
- Factual Accuracy (orange): When the corpus itself is outdated, contradictory, or incomplete, the answer inherits those flaws.
Interactions:
- Hover any card to reveal its mitigation strategies, which slide in below the impact statement.
- Click the colored example box to expand a detailed walkthrough of the scenario, and click again to collapse it.
- A warning icon in the corner of each card marks it as a severity hotspot.
The bottom panel, "When RAG Isn't Enough," links forward to GraphRAG for multi-hop reasoning and relationship analysis.
Lesson Plan
- Name the failure: Present students with a flawed RAG answer and have them classify which of the three limitations it illustrates.
- Match the mitigation: For each card, ask students to predict the mitigations before hovering to reveal them.
- Severity ranking: Discuss which limitation is most dangerous in a given domain (for example, medical versus marketing).
- Bridge to GraphRAG: Use the bottom panel to motivate why a graph-based approach addresses the relationship and reasoning gaps RAG leaves open.