Founders: If You're Building In AI, You Need To Understand RAG

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Let’s get into it!

A lot of founders, unfortunately, make technical decision based on trends. They’ll wedge AI into a product when a Python library will do, or they’ll start designing an “agentic” product when basic automations would have been enough for the MVP.

The most recent bit of AI jargon that I’m seeing turn into an unnecessary trend: RAG. Founders think they want it, but few know what it actually means, how it would make their product better, or if they even need it at all.

So let’s fix that today. To make it fun, we will use a fake FashionTech startup in our code examples.

Before we get started it needs to be said: if you’re building in HealthTech or LegalTech, chances are you should NOT be using RAG. If you’d like a breakdown of why that is, reply to this email, and I’ll work on a separate post about that.

But for this post, here’s when you should use RAG:

What RAG Actually Is

RAG stands for Retrieval-Augmented Generation.

That sounds more intimidating than it is.

In plain English, RAG means your product looks up relevant information before the AI responds. Then it passes that information to the model so the answer is grounded in something real instead of relying only on the model’s general knowledge.

That’s the whole idea.

A user asks a question.
The system retrieves the most relevant information.
That information is added to the prompt.
The model generates an answer using it.

So if a shopper asks, “Which dress from your catalog is best for a summer wedding if I want something flattering, sky blue, and not too tight in the waist?” and your product first pulls in product details, fit notes, customer reviews, and sizing guidance before answering, that is RAG.

If the model just answers from general knowledge without retrieving anything from your business, your users, or your product data first, that is not RAG.

This is where founders get confused. A lot of people talk about RAG as if it automatically means vector databases, embeddings, and complicated AI infrastructure. Sometimes it does involve those things. But the core concept is much simpler:

retrieve first, generate second.

And one important note: needing grounded answers does not automatically mean RAG is the right solution for every product. In high-trust spaces like legal and healthcare, naive RAG can create real problems. But in lower-risk products, especially ones where users are asking questions that depend on catalog data, reviews, or preferences, it can be a very useful pattern.

So before we get into when to use it, let’s make the distinction even clearer: what counts as RAG, and what doesn’t?

What Is Not RAG

Let’s start with what RAG is not, because this is where a lot of founders get tripped up.

Just because a product uses AI does not mean it is using RAG.

If your model is generating something from general knowledge, creativity, or a simple prompt, that is just AI output. It may still be useful. It may still be a great feature. But it is not RAG.

The easiest way to tell is to ask:

Does the system need to look something up before it can give a good answer?

If the answer is no, you probably are not dealing with RAG.

That distinction matters because a lot of founders hear “AI” and assume they need the most complex architecture available. They do not. If the feature is really about content generation, categorization, summarization, or a lightweight recommendation flow, a much simpler setup may be enough for the MVP.

What RAG Looks Like In Practice

Now let’s look at a case where RAG actually makes sense.

Say our fake FashionTech startup sells dresses online, and a shopper asks:

“Which dress would be best for a summer wedding if I want something flattering, sky blue, and not too tight in the waist?”

That is no longer a pure content-generation problem.

To answer that well, the system may need to retrieve real information like:

• product attributes

• fabric details

• fit notes

• size guidance

• customer reviews

• maybe even return feedback or shopper preferences

If the AI pulls that information first, and then uses it to generate an answer, that is RAG.

So instead of giving a generic answer like:
“Wrap dresses and A-line silhouettes are often flattering”

the product can give a much more useful answer, like:
“The Isla Wrap Midi Dress looks strongest because it has an adjustable waist, is sky blue, and multiple reviews mentioning that it felt comfortable through the midsection.”

That answer is better because it is grounded in the startup’s actual product data, not just the model’s general knowledge.

This is where RAG becomes valuable. It helps the product move from generic advice to grounded, product-specific guidance.

And that is often the difference between an AI feature that feels like a gimmick and one that feels genuinely useful.

In FashionTech, this can be especially helpful for things like:

• fit recommendations

• catalog-based product comparisons

• summarizing what reviews say

• answering shopper questions about materials, sizing, or comfort

• making recommendations based on preferences or past behavior

So the real value of RAG is not that it sounds advanced. The value is that it helps the AI answer using information your product already has.

Which brings us to the more important founder question:

When do you actually need that?

When You Probably Do Not Need RAG

This is the part most founders should spend the most time on.

Just because RAG exists does not mean it belongs in your MVP.

In a lot of early-stage products, the real goal is to validate whether users want the workflow at all. And if that is where you are, adding retrieval too early can create extra complexity before you have proven the feature is worth building.

You probably do not need RAG yet if your product is mostly doing things like:

• generating content

• rewriting or summarizing text

• brainstorming ideas

• helping users think through options

• creating first drafts

• running lightweight quizzes or guided flows

• producing recommendations that do not depend on your actual database

Using our FashionTech example, you probably do not need RAG if your AI feature is:

• writing product descriptions

• generating launch emails

• suggesting campaign ideas

• creating style quiz copy

• helping a founder name collections or categories

• turning a brand mood into marketing language

All of those can be useful. All of them can make the product feel smarter. But none of them necessarily require the system to retrieve real business data before answering.

That means you may be able to get to MVP faster with a much simpler setup.

And that matters.

Because the earlier you are, the more dangerous it is to build based on what sounds impressive instead of what users actually need. A lot of founders reach for RAG because it feels like the “serious” AI architecture. But if the core value of the feature does not depend on grounded answers, you may just be adding complexity, cost, and implementation time without improving the experience in a meaningful way.

A good rule of thumb is this:

If the answer can still be useful without looking up anything specific from your product, your users, or your business data, you probably do not need RAG yet.

That does not mean you will never need it. It just means it may not belong in version one.

So when does it start to matter? When the answer becomes meaningfully better because the system can retrieve real information first.

See you next week,

LaToya