Why AI Models Need A Tune Up

Software runs. Much like a motor car, software applications are initiated , driven forwards to execute their functions , steered in various directions depending on the user’s requirements and sometimes accelerated , remodelled and eventually stopped.

To drive the analogy home one more time, much like running a vehicle, a business needs to know that its LLM is operating at peak performance and efficiency at all times. While there is always testing and maintenance in the software industry, we don’t always think about this process with the same rigor that a good mechanic might apply to a vehicle inspection test.

“This is meaningful stuff especially when you consider our increasing reliance on LLM providers like OpenAI or Anthropic,” said Mansukhani, speaking to press & analysts at a deep dive summit this month. “As companies scale up the use of prompt engineering and Retrieval-Augmented Generation - , they rapidly increase the number of queries they send to these LLMs. As expected, the more advanced LLMs, like GPT-4 or Anthropic’s Claude Opus, cost more money to use.

The Domino team researchers also utilized prompting techniques specifically for reasoning tasks - Chain of Thought prompting and Program of Thought . CoT encourages LLMs to explain their reasoning steps. This enhances accuracy for complex tasks. PoT extends this by getting the models to output program code-like logic, which sharpens the model's reasoning capabilities.

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Large Language Model Data Generative AI Domino

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