Intermediate LM Skill Layer for Dynamic Prompt Orchestration
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Solution Overview
Problem
Existing language models (LLMs) face challenges with high computational resource utilization due to user prompts with additional tokens, leading to increased computational costs and hallucinations, and manual prompt engineering is limited by token size and scalability issues.
Innovation Solution
An intermediate LM skill layer dynamically determines target LM skills based on user inputs and context, using an orchestration loop to select appropriate skills without including them in the initial prompt, reducing computational burden and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual prompt engineering is used to improve LLM accuracy, then hallucination is reduced, but token size increases and scalability is limited
Solution Approach 1:
The system segments the prompt engineering task into multiple components: an intermediate layer separates the user's initial prompt from the LLM processing, dynamically determining and injecting only the necessary skill-specific prompt components. This segmentation allows the system to maintain high accuracy through structured prompt engineering while minimizing the total token count by avoiding redundant instructions.
Solution Approach 2:
The system implements dynamic prompt engineering where the intermediate layer adaptively determines which skills are relevant to each user input and dynamically constructs the prompt accordingly. This dynamic approach replaces static manual prompt engineering, allowing the system to use only the necessary tokens for each specific task rather than including all possible skill instructions, thus improving scalability and reducing token size.
2Measurement precision
If additional tokens are included in user prompts to improve model understanding, then task accuracy is enhanced, but computational resource utilization increases
Solution Approach 1:
The intermediate layer applies partial action by selectively including only the necessary skill-specific prompt components rather than providing complete or excessive instructions. The system determines the minimum required prompt elements needed for accurate task execution, avoiding unnecessary tokens that would increase computational burden while maintaining sufficient understanding accuracy.
3Adaptability or versatility
If the number of LM skills is increased to improve system capabilities, then versatility is enhanced, but prompt input space limitations are exceeded
Solution Approach 1:
The system performs preliminary action by pre-defining and cataloging multiple LM skills in the intermediate layer before user interaction. When a user provides input, the intermediate layer quickly determines which pre-defined skills are relevant and activates only those specific skills. This approach allows the system to maintain a large repertoire of capabilities (high versatility) while using minimal prompt input space, as the skill definitions are prepared in advance rather than included in each prompt.
Data Source
AI summary
Various embodiments of the technology described herein dynamically determine at least one target LM skill to use to generate an output for an initial prompt without the need for the target LM skill to be included in the original prompt. Embodiments of the technology described herein perform this determination via an intermediate LM skill layer that implements an orchestration loop in a computationally efficient manner that reduces effects of hallucination by identifying one or more target LM skills based on each task identified in the initial prompt. Embodiments of the intermediate LM skill layer are separate from the user device and the LLM. For example, the intermediate LM skill layer is positioned between an LLM abstraction layer and an application layer by which a user can interface with the intermediate LM skill layer.