What are some common pitfalls to avoid when developing an AI product from scratch?

Question

Many have ventured into the world of AI product development before. I am  eager to learn from your experiences, both successes and failures.  Understanding the common pitfalls faced by you will equip me with valuable knowledge to make informed decisions during my own AI development journey.

Accepted Answer

Common pitfalls include inadequate data quality, lack of clear objectives, overfitting models, ignoring ethical considerations, and failing to involve domain experts. It’s crucial to have a well-defined problem, high-quality data, and a multidisciplinary team to ensure successful AI product development.

Tyrone Robb · Answer

@sajin_s you are welcome

Tyrone Robb · Answer

Ideally you are injecting AI in to a pre established process or tool, whether something you were already doing or annoyed with the current solutions.

If you are one or two steps removed from what a big AI company is doing right now you may not have long left until what  you are doing becomes a core feature of the platform.

For example voice, I have seen a few companies who do voice related things chat will be able to do it. Their solution may be better, but it will be hard to 1) keep ahead of chat, and 2) convince customers you are somehow solving their problems in a more valuable way.

Another example is some of the pdf companies, they had a head start, they worked on chunking etc but not its a core feature.

We have seen from OpenAI that a vector db of some description is coming, while audio, images and text are all there.

I have spoken to a few companies on our podcast that are doing things that chat probably won't be able to do ever, like build and host a website (zero copy and paste), or manipulate an image with a stylus, or work with Cluade in the same dashboard.

Gurkaran Singh · Answer

Ah, diving into AI product development? Watch out for the perilous pitfalls like overfitting models - it's like squeezing into skinny jeans after Thanksgiving dinner, not a good fit!

Mason Derek Holloway · Answer

Make sure to clearly define your problem and data requirements early on. Skipping this step can lead to major issues down the line. Also, keep in mind the quality and quantity of your data; it's crucial for training effective models. I've seen teams get stuck because they didn't have enough diverse data or their data was biased. Finally, always test your model in real-world scenarios before launch to avoid unexpected failures.

Sajin S · Answer

@thomas_hansen5  Spot on! Inadequate data is a major roadblock - "garbage in, garbage out" as they say.  Having clear goals from the start is essential to guide the AI's development.  Overfitting can create a product that works well on training data but fails in the real world.  And you're right, ethics can't be an afterthought -  including domain experts from the beginning ensures a more well-rounded and responsible AI product. Thank you so much for these insights.

Sajin S · Answer

Thank you so much @masonderekholloway for those insights! Yes, I agree having enough and quality data is really important for training really effective models.

Sajin S · Answer

@thomas_hansen5 Unfortunately, I am still taking baby steps and have no clue yet.

Answer

@sajin_s NP, happy to help. BTW, we've spent insane amounts of energy to increase the quality of our scraping technology to eliminate the whole "garbage in" parts. Do you have suggestions for how to further reduce it?

Sajin S · Answer

@thestarkster Haha thanks for that example and yes I agree with you. This has been one feedback/suggestion I have been getting from many.

What are some common pitfalls to avoid when developing an AI product from scratch?

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