Large language models (LLMs) have revolutionized how we interact with technology, generating human-like text for everything from writing emails to crafting poems. But even the most advanced LLMs stumble when faced with complex questions requiring deep knowledge. They often hallucinate or get sidetracked by irrelevant information, limiting their real-world usefulness. Researchers are tackling this challenge head-on, exploring innovative ways to make LLMs truly knowledge-intensive. One promising approach is the Retrieve-Plan-Generate (RPG) framework, which takes inspiration from how humans solve problems. Think about researching a complex topic—you don't just absorb every piece of information you find. First, you identify the key questions, then plan how to answer them, and finally retrieve the specific information you need. RPG works similarly. It equips LLMs with the ability to plan their responses, retrieve relevant knowledge iteratively, and then generate accurate, comprehensive answers. Instead of being overwhelmed by long documents, the model focuses on specific topics, ensuring the generated text stays on track. This iterative approach allows LLMs to delve deeper into complex issues and generate more nuanced and accurate answers, minimizing the risk of factual errors or going off-topic. RPG also employs a clever technique called multi-task prompt tuning. This allows the model to learn both planning and answering efficiently, making the most of its existing capabilities. Results on various tasks—from answering multi-hop questions to crafting long-form articles—show that RPG significantly outperforms traditional methods, demonstrating its potential to unlock a new level of knowledge-intensive AI generation. While the research shows impressive results, challenges remain. Scaling the framework to even larger models and more extensive datasets is crucial for achieving real-world impact. The future of LLMs may hinge on such innovative approaches, bridging the gap between vast amounts of information and intelligent, focused generation.