Deep Papers DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
Jul 23, 2024
Cyrus Nouroozi, a core contributor to DSPy and co-founder of Zenbase, dives into the innovative world of language models. He explains DSPy assertions that help enforce computational constraints, enhancing reliability in language model applications. The discussion reveals that these assertions can significantly boost compliance and improve output quality. Cyrus showcases practical examples like tweet generation and contrasts DSPy's robust approach with traditional prompt engineering. The episode wraps up with insights into optimization strategies and the future of LLM pipelines.
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Program The What, Not The How
- DSPy treats LLM pipelines like modular neural networks where you define the task, not the exact prompts.
- Optimizers then search prompt/example combinations to maximize a metric across the pipeline.
Colleague Demo Made DSPy Intuitive
- Dat described DSPy as declaring desired inputs and outputs, then letting optimizers handle the prompt details.
- Cyrus confirmed the analogy to PyTorch modules and ML optimizers during the intro.
Place Constraints Early In The Pipeline
- Add assertions to check constraints early in pipeline steps so failures are detected and corrected sooner.
- Use suggestions for soft guidance and assertions for hard constraints like length or profanity.