Large Language Model
What is a large language model?
A large language model (LLM) is a type of AI algorithm that predicts and generates text one token—a word or word fragment—at a time. Like traditional machine learning models, LLMs process inputs through weighted parameters to produce outputs. However, instead of predicting categories or numerical values, they predict the next word in a sequence.
These models work by running input through many layers of weighted parameters—essentially variables that determine how much influence different aspects of the input have on the output. The "large" refers to the vast number of parameters these models contain, often billions or trillions, not to a fundamental difference in how they operate compared to simpler neural networks.
How are large language models similar to other machine learning models?
Large language models function fundamentally like other machine learning systems. They take inputs, process them through weighted parameters, and generate outputs. The core concept resembles simpler machine learning models, just scaled up dramatically in complexity.
Like traditional machine learning models that might predict customer churn or lifetime value, LLMs make predictions—just about what word comes next rather than about numerical outcomes. The algorithm tweaks these weights to minimize the difference between predicted and actual results, the same way simpler models optimize their parameters.
Why does the non-deterministic nature of LLMs matter?
Large language models are stochastic systems with non-deterministic outputs, meaning they don't produce identical responses every time, even with the same input. This characteristic requires the same measurement and systematic improvement approaches used for traditional machine learning models.
Teams can't just build an LLM-based product and assume it works correctly. They need systematic ways to measure performance, identify failure modes, and improve results—similar to how data scientists and machine learning engineers have always approached building and refining models. The probabilistic nature of LLMs makes evaluation and debugging essential practices for any AI product development.
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