Developing Your AI Context as a Product

Context is King

You’ve probably encountered this term in the context of AI.

You won’t get far with AI, even with amazing prompting techniques, if AI doesn’t have the right context.

Many people focus on the Data aspect of context - what’s in our CRM, in our collaboration tools, in our ERP, in any of our vertical systems.

Another interesting aspect is the intent - what are we trying to achieve? What is our strategy? What problems are we focused on? Who are the players?

Some of that information could be ascertained by inhaling data from systems.

But as you’ve probably seen if you’re playing around with AI - it still has context limits, so there’s value in shaping and emphasizing.

For example - if I’m telling AI who are my ideal customers, what are the problems I’m focused on solving, what are the problems and opportunities I see in MY business, I’m getting much more value, even without advanced prompting.

Think of it this way - Context is like a platform product. By creating the right context, you’re reducing the cognitive load and expertise required for prompting, and getting better answers.

If Context is King, and is a product, it brings about an interesting question - How should we “develop” this product?

Do you already have someone responsible for Product Strategy for your AI Context?

Do you have a Product Goal for it?

A team that is focused on evolving it? Building an increment of the context, Shipping it (so people can use it), Sensing (whether its useful, using outcome-oriented leading indicators?), Responding?

Do you have a Context Backlog containing opportunities to improve the Context? (e.g. Connecting it to more data, shaping it using JTBDs, personas, OKRs, problem statements, training it)

What might it look like to apply product thinking and techniques in your AI transformation?

Frequently Asked Questions

What does it mean to treat AI context as a product?

It means intentionally designing and iterating prompts, knowledge, constraints, and integrations so people can reliably use AI to achieve meaningful outcomes.

Who should own AI context evolution?

Ownership should be explicit. In many cases, a product-minded cross-functional team is best positioned to evolve context based on usage evidence.

How do we measure whether context quality is improving?

Track practical signals such as output usefulness, task success rates, time-to-value, and reduction in prompting effort for key workflows.