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title: Programming, Not Prompting: A Hands-On Guide to DSPy
description: DSPy is a declarative framework by DataBricks that treats LLM applications as programming rather than manual prompting. The framework uses three core...
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# Programming, Not Prompting: A Hands-On Guide to DSPy

**[Towards Data Science](https://daily.dev/sources/tds)** · 16 min read · 3 upvotes · 0 comments

## Summary

DSPy is a declarative framework by DataBricks that treats LLM applications as programming rather than manual prompting. The framework uses three core components: language models, signatures (input/output declarations), and modules (prompting techniques like Chain-of-Thought or ReAct). Key features include automatic optimization through techniques like MIPROv2 that can improve accuracy by adjusting instructions and adding few-shot examples. The article demonstrates building applications from simple combinatorics problems to real-world NPS comment classification, showing significant accuracy improvements (62% to 82%) through automated optimization.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/programming-not-prompting-a-hands-on-guide-to-dspy/>

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Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#llm](https://daily.dev/tags/llm), [#prompt-engineering](https://daily.dev/tags/prompt-engineering)

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