Imagine asking a computer a really tricky question, like "Which CEO of a 2023 sustainability award-winning company is the oldest?" Large Language Models (LLMs), despite their impressive language skills, often stumble with these complex, multi-step questions that involve digging through massive knowledge graphs (KGs). Why? Traditional methods either underutilize the LLM’s reasoning power or become computationally expensive by tightly coupling the LLM to the KG. Now, researchers have developed EffiQA, a clever framework that balances performance and efficiency. EffiQA works like a well-coordinated team: the LLM acts as a strategist, creating a 'global plan' by breaking down the complex question into smaller, manageable parts and generating instructions. Then, a smaller 'plug-in' model acts as a specialized scout, efficiently exploring the KG based on the LLM's instructions, pruning irrelevant information. Finally, the LLM reviews the scout's findings, refining its plan and instructions in a 'self-reflection' phase. This iterative process leads to more accurate and efficient answers. Tested on various question-answering benchmarks, EffiQA shows it can navigate complex knowledge graphs effectively while keeping computational costs in check. This approach opens exciting possibilities for building more efficient, knowledge-powered AI systems that can tackle the most challenging questions. While EffiQA shows great promise, it still relies heavily on the reasoning capabilities of the LLM, meaning limitations in the LLM can impact the framework's performance. Also, scaling the 'plug-in' model to truly massive and complex KGs presents a challenge. However, EffiQA offers a compelling glimpse into the future of knowledge-intensive AI, paving the way for even more intelligent and resourceful systems.