What does research say about: Model downshifting lowers inference costs?

Structured prompts enable GPT-3.5-class models to match GPT-4 output quality on 78% of classification tasks, at 1/30th the per-token cost ($0.0005 vs $0.03/1K tokens). (Source: Khattab et al., 'DSPy: Compiling Declarative Language Model Calls', Stanford NLP, 2023). Without quality prompts, smaller models produce unusable output, forcing developers to default to expensive frontier models.

Model downshifting lowers inference costs. | Prompt Engineering Research

Context & Methodology

Without quality prompts, smaller models produce unusable output, forcing developers to default to expensive frontier models.

Applicable Use Cases

workflow

Applies To

openaianthropicgoogle

Primary Impact

cost

Confidence Level

High

Platform Status

Planned

Implementation Effort

medium

Recommendation

Execution Priority

Dependencies & Conflicts

Conflicts with:

econ-013

Put This Evidence to Work

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