Prompt Engineering Engine for Accurate LLM Code Generation
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Solution Overview
Problem
Existing systems struggle to efficiently generate prompts for large language models (LLMs) that elicit accurate and desired responses specific to a computing environment, often resulting in incorrect or incomplete code generation.
Innovation Solution
The implementation of a prompt engineering engine that processes user prompts, augments them with necessary context and configuration details, and submits these pre-processed prompts to LLMs to ensure accurate and relevant responses, tailored to the specific computing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a developer manually crafts prompts for LLMs, then the prompt can be optimized for specific computing environments, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by automatically generating and pre-processing prompts before they are submitted to the LLM. The prompt engineering engine incorporates computing environment details, configuration parameters, and best practices into the prompt in advance, eliminating the need for manual optimization while ensuring accuracy for the specific target environment.
2Productivity
If generic prompts are used for LLMs, then the process is simple and fast, but the code generated is incorrect or incomplete for specific computing environments
Solution Approach 1:
The system applies local quality by tailoring the prompt to specific computing environments and requirements. The prompt engineering engine incorporates environment-specific details, configuration parameters, and best practices into the generated prompt, ensuring that the code produced is both fast and accurate for the particular target environment rather than using a one-size-fits-all approach.
3Manufacturing precision
If the prompt includes detailed computing environment context, then the LLM response is more accurate, but the prompt becomes more complex and harder to manage
Solution Approach 1:
The prompt engineering engine acts as an intermediary between the developer's high-level requirements and the LLM. It automatically incorporates computing environment details, configuration parameters, and best practices into the prompt, managing the complexity of environment-specific context while keeping the developer's interface simple and the overall system maintainable.
4Productivity
If the system automatically generates prompts without environment context, then the process is efficient, but the code does not match the target computing environment requirements
Solution Approach 1:
The system implements dynamics by automatically adapting the prompt to the specific computing environment being targeted. The prompt engineering engine incorporates environment-specific details, configuration parameters, and best practices dynamically based on the target environment, enabling efficient automated prompt generation that remains highly adaptable to different computing environments without manual intervention.
Data Source
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AI summary
A system and method including receiving a prompt specifying at least one task type; determining a system prompt based on the received prompt, the system prompt including artificial intelligence (Al) system configuration details corresponding to the at least one task type; pre-processing the system prompt to generate a pre-processed prompt including code referenced in the system prompt; transmitting, as an input prompt, the pre-processed prompt to an Al system; receiving, in response to the Al system executing the pre-processed prompt, a result from the AI system; and storing a record of the result from the AI system in a data repository.