Imagine an AI that can not only "see" images but also tap into vast knowledge bases to answer complex questions. That's the fascinating world of Knowledge-Based Visual Question Answering (KVQA). Current KVQA systems face a challenge: dealing with intricate questions that involve multiple sources of information. They often struggle to pinpoint the exact knowledge needed and can get bogged down by irrelevant details. A new research project called DKA (Disentangled Knowledge Acquisition) offers a clever solution. DKA acts like a skilled detective, breaking down complex questions into simpler sub-questions. One sub-question focuses on the image itself, guiding the AI's "vision" to relevant details. The other sub-question delves into the external knowledge base, searching for specific facts. This two-pronged approach helps avoid confusion and provides the AI with precisely the information it needs. DKA doesn't require any additional training – it works with existing AI models like Large Language Models (LLMs). The results are impressive: DKA outperforms state-of-the-art methods on benchmark datasets like OK-VQA and AOK-VQA. Think of it as giving an LLM a magnifying glass and a library card, empowering it to solve visual puzzles more effectively. This advancement has real-world implications for everything from search engines to medical diagnosis. While there's still room for improvement in handling complex visual scenes and further refining the knowledge retrieval process, DKA opens exciting new possibilities for more intuitive and insightful AI interactions.