How LLMs can optimize their own prompts

Large language models (LLM) have peculiar sensitivities to the way their prompts are formulated. The same command with different prompt formulations can yield completely different results.

Usually, developers and researchers use trial and error to find prompt engineering techniques that can increase the performance of the LLMs.

An alternative approach is to allow LLMs to optimize their own prompts and discover the most effective instructions to enhance their accuracy. This concept forms the basis of Optimization by PROmpting (OPRO), a simple yet powerful method developed by Google DeepMind to use LLMs as optimizers.

OPRO uses a meta-prompt, an optimizer LLM, and an evaluator LLM to automatically find strings that can enhance the model’s performance when added to the prompt.

To learn more about OPRO and how to use it with your own models, read the full article on TechTalks.

For more on AI research:

• Emotional prompts enhance language models, study finds

• Can ChatGPT self-explain its predictions?

• Does your enterprise need a vector database for LLM search?

• Why universal adversarial attacks against LLMs are cause for concern

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