US 2026/0310980 A1 · AUTOMATIC TUNING OF LLM PROMPTS

Abstract

The technology described herein automatically tunes large language model (LLM) prompts for improved performance. The system receives an LLM prompt as input, optimizes it, and outputs a more effective prompt. The optimization process involves converting the prompt into a hierarchical prompt tree, which organizes the prompt into distinct sections and subsections. This structured format allows for systematic refinements targeting specific components without altering the entire prompt. The system uses an actor/critic learning model, where the critic evaluates the prompts generated by the actor and provides feedback to guide future prompt generation decisions. The actor executes actions based on the feedback, continuously improving its performance. The iterative process generates replacement prompts, assigns quality scores, and selects high-quality prompts for further optimization.

Claims20

Dependent claims

One or more computer storage media comprising computer-executable instructions that when executed by computing device performs a method of tuning a prompt, the method comprising:

receiving a prompt;
generating a hierarchical prompt tree from text in the prompt;
providing the hierarchical prompt tree, a large language model (LLM) output generated in response to the prompt combined with an input, and an expected output to a critic model, wherein the input includes actual values that are inserted in placeholders within the prompt;
generating, at the critic model, a reflection;
providing the reflection and the hierarchical prompt tree of text to an actor model;
generating, by the actor model, a revised hierarchical prompt tree;
generating a revised prompt from the revised hierarchical prompt tree;
and
storing the revised prompt.

6 more elements

Dependencies are inferred from the claim text.

Automatic tuning of llm prompts

Life of this patent

Automatic tuning of llm prompts

Life of this patent

Abstract

Claims20