Treat AI Like Backend Infrastructure — Not a UI Feature

Most builders still treat AI like a chatbot.

Drop it in a corner. Let it generate copy.
Maybe bolt on a GPT-4 button for show.

That’s fine… but it’s small thinking.

If you want real leverage as a solo builder, start treating AI like infrastructure - not just a prompt box.

Here’s what that unlocks 👇

⚙️ 1. AI as an Internal API Layer

The most powerful use cases of LLMs aren’t visible.

I use them like microservices that:

• Interpret natural language inputs

• Route decisions

• Summarize or transform data

• Trigger the right actions downstream

They're stateless, interchangeable, and abstracted from the UI - just like a smart backend layer.

🧩 2. Function Calling = Logic as a Service

LLMs aren't just predicting text, they're dispatching entire logic loops.

Using function calling, I can wire models to:

• Fetch real-time data

• Update a Notion doc

• Hit an external API

• Trigger workflows in Zapier or Vercel

AI becomes the brain, but real-world execution happens elsewhere. Just like microservices coordinating a product flow.

Think of AI as a conductor, not as the wheel. It doesn’t do the work, it orchestrates it.

🧠 3. Memory = Modular, Not Monolithic

I don’t trust long chat history as memory (and you shouldn’t either). Instead, I use:

• Vector stores for semantic recall (e.g., Pinecone, Weaviate, Neo4J)

• Tagged snippets for user/system context

• External sources of truth (Notion, JSON blobs, etc.)

It’s less like “ChatGPT remembers me” and more like “this app fetches only what matters — on demand.”

Think about how your brain works after a coworker asks for help with a project.

You don’t replay every water cooler convo or meeting from the past year. You pull up just the right context: the docs, decisions, and priorities that actually matter for the task.

That’s how AI memory should work - focused, relevant, and fast.

🧪 4. SLMs > LLMs (in many cases)

I route tasks to smaller, faster Small Language Models (SLMs) when possible.

Need to:

• Classify a task?

• Extract a field?

• Rewrite an error message?

• Accomplish any simple, repetitve task?

You don’t need GPT-4. You need an SLM that costs 1/10th the price and works in 100ms.

Speed isn’t just a UX bonus - it’s the entire difference between a magic feature and a laggy one.

🛑 Bottom Line: Stop Building AI Products. Start Architecting AI Systems.

Most people are stuck on prompting.

The real unlock is thinking like a backend engineer:

• What’s the input?

• What context do I need?

• What decision needs to be made?

• What downstream system executes it?

You’re not designing features, you’re designing intelligence.

Did You Know? The average cloud weighs over a million pounds! It floats because it’s dispersed - same with ideas.

‘Till next time,

Stack & Scale

P.S. If this sparked something in you, help someone else level up and share this newsletter with a builder who needs to read it.