# 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!