Large language models (LLMs) have gained attention and recognition for their revolutionary capabilities in natural language processing. The applications of LLMs are varied and extensive. Developers and researchers have been exploring applications ranging from content creation to conversational agents and data analysis. However, these LLM applications' capabilities largely depend on their context length. The announcement of Google’s Gemini 1.5 Pro, with an input context length of 10 million tokens, ushers in an era of increased capabilities and applications of LLMs. Before creating or integrating increased context LLMs into current applications or creating new applications to leverage this new capability, developers and software engineers must understand the concept of context length and how it can affect their design choices.
Context Length Explained
The context length of an LLM represents the maximum number of tokens it can consider or process. A token is the numeric …