Imagine an AI assistant that can effortlessly tap into a vast toolbox of specialized skills, seamlessly switching between them to tackle complex tasks. Researchers at PricewaterhouseCoopers are working on just that with their innovative approach called "Toolshed." Current large language models (LLMs), while impressive, have limitations when it comes to using external tools or APIs – the building blocks of real-world applications. They struggle to select the right tool for the job, especially when faced with thousands of options. Toolshed overcomes these challenges by creating a smarter way to organize and access these tools. Think of it as giving your AI a well-organized and easily searchable toolbox. Instead of simply listing tools, Toolshed enriches the description of each tool with key information, including hypothetical questions the tool can answer. This helps the AI understand the tool's purpose and capabilities better. Then, when faced with a user request, Toolshed uses advanced techniques to break down the request into smaller tasks and match them to the most relevant tools. The magic lies in its ability to handle complex queries, not just simple instructions. It can take multi-step requests and intelligently figure out which tools are needed at each step. This approach, called Advanced RAG-Tool Fusion, results in a significant boost in accuracy, selecting the correct tools up to 46% more often than existing methods. What's even more exciting is that Toolshed doesn't require retraining the entire AI model. This plug-and-play approach makes it adaptable to new LLMs and tools, ensuring that your AI can always access the latest and greatest resources. Toolshed opens doors to a new era of powerful, tool-equipped AI assistants. Imagine AI effortlessly handling database interactions, coordinating multi-agent code development, or answering complex questions by accessing domain-specific knowledge bases. Toolshed brings these possibilities closer to reality.