By integrating with external databases, APIs, and simulation tools, Large Language Models (LLMs) significantly expand their capabilities beyond simple text generation. This connection allows them to actively interact with, and in some situations influence or control, external systems—such as fetching real-time financial data or accessing healthcare records. As a result of these integrations, LLMs are able to operate more like autonomous agents.

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In Large Language Models (LLMs), integrating external tools is a practical application of problem decomposition. Since LLMs may lack real-time or highly specific accurate data (like stock prices or weather updates) after training or fine-tuning, they can be connected with external APIs. To fulfill a complex query, the LLM decomposes the task into sub-problems: it handles parts of the query internally while generating specific markers to call external tools for the remaining sub-problems, effectively merging internal generation with external data retrieval.

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