# DSPy

DSPy is a Python framework for building AI systems, providing structured signatures, modules, and optimizers for maintainable and optimizable programs.

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## The Real Python Podcast - Episode #306: Programmatically Developing LLM Prompts With DSPy

DevFeed: [The Real Python Podcast - Episode #306: Programmatically Developing LLM Prompts With DSPy](<https://devfeed.tech/articles/the-real-python-podcast-episode-306-programmatically-developing-llm-prompts-with-dspy-4390.md>)

Original publisher: [Read original article](<https://realpython.com/podcasts/rpp/306/>)

Author: Real Python

Published: 2026-08-07T12:00:00Z

Content type: article

Language: en

Sources: [Real Python](<https://devfeed.tech/sources/real-python.md>)

Topics: [DSPy](<https://devfeed.tech/topics/dspy.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Python](<https://devfeed.tech/topics/python.md>), [llamaindex](<https://devfeed.tech/topics/llamaindex.md>)

Tags: [dspy](<https://devfeed.tech/tags/dspy.md>), [llamaindex](<https://devfeed.tech/tags/llamaindex.md>), [llm](<https://devfeed.tech/tags/llm.md>), [podcast](<https://devfeed.tech/tags/podcast.md>), [programming](<https://devfeed.tech/tags/programming.md>), [prompt-engineering](<https://devfeed.tech/tags/prompt-engineering.md>), [python](<https://devfeed.tech/tags/python.md>), [rag](<https://devfeed.tech/tags/rag.md>)

### AI overview

This podcast episode explains how DSPy programmatically develops and optimizes prompts for LLM applications. It covers declarative signatures, prompt compilation and tuning, and the limitations of manual prompt engineering. The episode also highlights a course on using LlamaIndex for RAG in Python.

### Source excerpt

How can you move from manually writing prompts for an LLM application toward defining them programmatically? This week on the show, Brett Kennedy returns to discuss his new book "Building LLM Applications with DSPy."

## How we used DSPy to turn AI evaluations into better responses in Dash chat

DevFeed: [How we used DSPy to turn AI evaluations into better responses in Dash chat](<https://devfeed.tech/articles/how-we-used-dspy-to-turn-ai-evaluations-into-better-responses-in-dash-chat-177.md>)

Original publisher: [Read original article](<https://dropbox.tech/machine-learning/how-we-turned-ai-evaluations-into-better-responses-in-dash-chat>)

Author: Yasmin McDowell,Lawrence Good,Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon

Published: 2026-06-25T16:30:00Z

Content type: article

Language: en

Sources: [Dropbox Tech Blog](<https://devfeed.tech/sources/dropbox-tech-blog.md>)

Topics: [DSPy](<https://devfeed.tech/topics/dspy.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [dash](<https://devfeed.tech/tags/dash.md>), [dspy](<https://devfeed.tech/tags/dspy.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [llm](<https://devfeed.tech/tags/llm.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [responses](<https://devfeed.tech/tags/responses.md>), [tools](<https://devfeed.tech/tags/tools.md>)

### AI overview

Dropbox describes using DSPy and evaluation feedback to improve Dash chat. Human-labeled examples calibrate LLM judges, whose scalable signals then optimize the chat agent's system prompt, reducing incomplete answers and token usage without compromising answer quality.

### Source excerpt

We used DSPy to improve LLM judges and optimize our chat experience, creating an evaluation-driven feedback loop that produced better outputs.

## AI Engineering Primer

DevFeed: [AI Engineering Primer](<https://devfeed.tech/articles/ai-engineering-primer-33448.md>)

Original publisher: [Read original article](<https://timkellogg.me/blog/2024/12/19/ai-primer>)

Published: 2024-12-19T00:00:00Z

Content type: tutorial

Language: en

Sources: [Tim Kellogg](<https://devfeed.tech/sources/tim-kellogg.md>)

Topics: [AI Engineering](<https://devfeed.tech/topics/ai-engineering.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [DSPy](<https://devfeed.tech/topics/dspy.md>)

Tags: [advice](<https://devfeed.tech/tags/advice.md>), [ai-engineer](<https://devfeed.tech/tags/ai-engineer.md>), [ai-engineering](<https://devfeed.tech/tags/ai-engineering.md>), [dspy](<https://devfeed.tech/tags/dspy.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [learning](<https://devfeed.tech/tags/learning.md>), [llms](<https://devfeed.tech/tags/llms.md>), [prompt-optimization](<https://devfeed.tech/tags/prompt-optimization.md>)

### AI overview

A primer on getting up to speed with AI engineering. It discusses the field's rapid evolution and advises readers to be cautious with LangChain tutorials, prompt influencers, and blindly applying traditional machine-learning advice. It mentions alternatives including hand-written code, LlamaIndex, DSPy, Gradio, and vendor SDKs.

### Source excerpt

How do you get up to speed with AI engineering? Unfortunately, I don't know of any good consolidated resources, so I'm going to attempt to make one here. My first attempt at this focused more on what an AI engineer is and made only a feeble attempt at providing resources to get started. Let's go!