Get Your AI Drive On

As large language models (LLMs) mature, business and product leaders are under increasing pressure to integrate AI into their products. But with a technology as new and mind boggling as LLMs, it's not surprising that many don't know where to start.

Here's the good news: You don't have to have a deep understanding of language modeling to own an AI product. What you do need to do, however, is take a serious look at your organization to understand whether or not you and your team are ready for the unique challenges of building products in the age of artificial intelligence.

It's time to stop thinking about AI and start building. Here are some tips to get you started.

AI: a tool, not a product

We've all had our "wow" moment with LLMs. Whether it's code completion and creation, copy editing, creative writing, or interactive customer service, these models can perform so many tasks right out of the box. But if you're going to start building real products with AI, you need to get over that initial awe and start seeing LLMs as a tool – albeit an impressive and super-powerful one.

There is much more to building an effective product that your customers will want to use than just the AI technology itself. Aspects such as a clearly defined business use case (ideally supported by user research), a well-designed user interface, and possibly additional data to augment the LLM are just as important as the model itself.

Effective leadership → product mindset

LLMs are readily available, either through proprietary vendors such as OpenAI and Cohere, or in the form of open source models. Your team takes it from there: planning your AI adoption is all about envisioning how to integrate AI functionality into your product so that it can deliver tangible benefits to users.

To do this, AI owners need to adopt a product mindset. That means understanding what you want the technology to do for you – and what tools, skills, and resources your team needs to make it happen.

Start ASAP and iterate quickly

With new models being released every week, long development cycles are not an option. Instead, you need to stay agile in your workflow and flexible in your product design.  

Start building quick and dirty prototypes (you can do it in a matter of hours with platforms like deepset Cloud) and make sure you get them in front of your users as soon as possible. Test them, evaluate them – and then go back and refine them. That way, you'll make sure you're building a product that actually addresses your users' needs.  

And by taking a modular, model-agnostic approach, you ensure that you can always plug in the LLM that best solves your problem.

Join the discussion

To learn more about the unique challenges of AI adoption and how to address them, join us for our webinar, Taking charge in AI application delivery.

In the webinar, thought leaders Milos Rusic of deepset and James Governor of RedMonk will share their thoughts from watching hundreds of teams struggle with AI adoption – and offer practical advice on how to ensure your team is on the path to success.

If you're a current AI product owner, working on an AI team, or looking to adopt AI in the near future, don't miss this session - it's sure to be packed with unique insights into strategies for building and delivering successful AI products.