# What 300+ Engineers from Netflix, Amazon, and Instacart Asked About AI Engineering

DevFeed: [What 300+ Engineers from Netflix, Amazon, and Instacart Asked About AI Engineering](<https://devfeed.tech/articles/what-300-engineers-from-netflix-amazon-and-instacart-asked-about-ai-engineering-28615.md>)

Original publisher: [Read original article](<https://www.marvelousmlops.io/p/what-300-engineers-from-netflix-amazon>)

Author: Hugo Bowne-Anderson

Published: 2026-03-06T07:03:44Z

Content type: article

Language: en

Sources: [MarvelousMLOps](<https://devfeed.tech/sources/marvelousmlops.md>)

Topics: [AI Engineering](<https://devfeed.tech/topics/ai-engineering.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [Testing](<https://devfeed.tech/topics/testing.md>)

Tags: [ai-engineering](<https://devfeed.tech/tags/ai-engineering.md>), [context-engineering](<https://devfeed.tech/tags/context-engineering.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [llms](<https://devfeed.tech/tags/llms.md>), [testing](<https://devfeed.tech/tags/testing.md>)

## AI overview

This article presents the top 10 questions and answers gathered from four cohorts of a Building AI Applications course attended by more than 300 builders from companies including Netflix, Amazon, and Instacart. The supplied excerpt details how to improve reliability and consistency in LLM applications through prompt and context engineering, structured outputs, validation, evaluation, and testing.

## Source excerpt

The Top 10 questions (and answers) from 4 cohorts of Building AI Applications