A Practical Roadmap to AI Fluency for Hardware Designers

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Read time: 10 mins

Here is a question on many minds right now: what role does AI play in the role of a hardware engineer today?

The word “AI” elicits two opposite reactions in people: excitement or indifference.

Those in the excitement camp generally embrace AI quickly. I know people who subscribed to the paid ChatGPT version early and have used it ever since, well before most companies formalized AI policies in the workplace. These early adopters I know used it for writing emails, product documentation, and brainstorming marketing ideas.

On the opposite end, many engineers tell me they don’t quite see where AI fits in hardware design. In the early days of ChatGPT, some tried to use it simply as a better Google search. But models weren’t mature and their prompts often missed the mark. Over time we’ve learned that an AI chat box is not the same as a search bar. Today, prompting is seemingly an art form. People now build meta-prompts (prompts to generate prompts).

But for the engineer doing highly technical work, the question remains: where does AI realistically help? If your day is spent in semiconductor tools without AI features, how do LLMs or ML fit into your workflow?

Start-ups are exploring this, but it’s not yet standard. Meanwhile, executives want an “AI checkbox” to show they’re on the trend. Between hype and practicality, what should an individual contributor do to stay relevant?

In today’s post, we will answer these questions and at the end, provide a practical roadmap you can start using today if you are a hardware engineer who is still on the fence on learning and using AI. I tend to err on the side of thorough understanding. My recommendations in this post will go beyond just the surface of learning to prompt chatbots.

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