Prompt Chain
Prompt chaining is a systematic approach in artificial intelligence where complex tasks are broken down into smaller, sequential steps, with each step's output serving as input for the subsequent step. This methodology has gained prominence in the field of Large Language Models (LLMs) as organizations seek to improve output quality and maintain better control over AI-generated content.[1]
History
The concept of prompt chaining evolved from earlier work on chain-of-thought prompting, first formally described by Wei et al. in 2022[1]. The technique gained wider attention following demonstrations of its effectiveness in complex reasoning tasks[2]
Theoretical foundation
The effectiveness of prompt chaining builds upon research in:
Chain-of-thought reasoning[1] Zero-shot task decomposition[2] Self-consistency in language models[3]
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Types
Research has identified several approaches to implementing prompt chains:
Individual prompt chains
These utilize a single LLM throughout the process, similar to the methodology described in chain-of-thought reasoning studies.[1]
Multi-model approaches
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Limitations
Current research identifies several limitations:
Potential error propagation between chain steps[3] Computational overhead in multi-step processing Challenge of maintaining context across chain steps
See also
Prompt engineering Large language model Natural language processing
References
- ↑ 1.0 1.1 1.2 1.3 Wei, Jason, et al. "Chain of thought prompting elicits reasoning in large language models." arXiv preprint arXiv:2201.11903 (2022)
- ↑ 2.0 2.1 Kojima, Takeshi, et al. "Large language models are zero-shot reasoners." arXiv preprint arXiv:2205.11916 (2022)
- ↑ 3.0 3.1 Wang, Xuezhi, et al. "Self-consistency improves chain of thought reasoning in language models." arXiv preprint arXiv:2203.11171 (2022)
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