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Progressive Enhancement(PE)
Incrementally improves AI output quality based on available resources and time
In 30 seconds
- What
- Delivers initial results quickly, then progressively refines output quality if time and resources remain available.
- When to use
- Interactive systems where users need fast feedback but benefit from better results if the agent has spare compute capacity.
- Watch out
- Intermediate outputs may mislead users into acting before refinement completes, or refinement may never finish under load.
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Progressive Enhancement: Overview
Incrementally improves AI output quality based on available resources and time
- Layered quality improvement
- Anytime algorithm implementation
- Resource-based enhancement
- Graceful degradation support
- Quality-time trade-offs
- User preference adaptation
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References
The papers, specifications, and repositories this pattern is based on.
- Zilberstein (1996): The principles of anytime algorithms
- Fast Inference from Transformers via Speculative DecodingarXiv:2211.17192
- Accelerating Large Language Model Decoding with Speculative SamplingarXiv:2302.01318
- Shinn et al. (2023): Self-Refine: Iterative Refinement with Self-Feedback
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