Common pitfalls when building generative AI applications
📰 Chip Huyen's Blog
Learn to avoid common pitfalls when building generative AI applications to ensure effective and efficient development
Action Steps
- Identify the problem you're trying to solve and determine if generative AI is the best approach
- Evaluate the complexity of the task and consider alternative solutions
- Assess the available data and its quality to ensure it's sufficient for training a generative model
- Consider the potential risks and biases associated with generative AI
- Test and validate your generative AI application thoroughly to ensure it meets the desired outcomes
Who Needs to Know This
Developers, data scientists, and product managers working on AI-powered projects can benefit from understanding these pitfalls to make informed decisions and avoid costly mistakes
Key Insight
💡 Don't use generative AI when you don't need it
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Full Article
As we’re still in the early days of building applications with foundation models, it’s normal to make mistakes. This is a quick note with examples of some of the most common pitfalls that I’ve seen, both from public case studies and from my personal experience. Because these pitfalls are common, if you’ve worked on any AI product, you’ve probably seen them before. 1. Use generative AI when you don't need generative AI</
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