The AI Engineering Stack
What Actually Powers an AI Product?
When we talk about AI, we often talk about the awesome things like ChatGPT writes an email in seconds, Spotify recommends the perfect weekend playlist or a travel agent types in "family trip to Thailand" and within seconds, a ready-made itinerary appears.
But if you’ve ever tried to build an AI-powered feature, you quickly realize that the AI model is just one part of the system. Behind the seemingly simple layer sits a complex stack of tools and infrastructure that most users and even many PMs never see.
If you're a product manager and you’ve ever felt overwhelmed by terms like LLMs, vector databases, or prompt engineering you’re not alone. You don’t need to be an ML engineer to build smart features. But you do need to understand the AI tech stack enough to ask the right questions, work with your AI/ML team, and spot real opportunities for incorporating AI in your product.
What is an AI Tech Stack?
Think of building an AI feature like building a smart home.
At the base, you’ve got the foundation - electricity, plumbing, the walls. Then come the tools - smart devices, switches, appliances. And finally, there’s the experience -the cozy, voice-controlled lights and music your guests see.
In AI, the foundation is the infrastructure, the appliances are the models and tools, and the experience is your product - the thing users interact with. That’s the layer you own as a PM.
The stack usually has these layers:
1. Infrastructure Layer
This is the engine room. It’s where all the AI computations happen. If you’ve heard someone mention about GPU costs are going crazy, they’re talking about this layer.
This layer consists of the following:
Cloud providers like AWS, Google Cloud, Azure
Powerful chips called GPUs (used to train and run models)
Systems to store and manage data
As a product manager, you should be aware that training or even running AI models (especially large ones) requires large compute power which comes with cost, latency, and reliability trade-offs.
Not every project you are working on needs expertise to configure GPU or run things on cloud. But you should question whether to use a pre-trained model instead of training our own? Can your users compromise on speed and real-time responses or can it be slower and cheaper for the use case you are working on.
2. Model Layer
This is where the smart part happens and works like a brain. Models are algorithms that have learned from tons of data. You can either build one yourself which can be rare and expensive or use existing ones. We will go in depth in another post on how models learn.
Some of the more popular models that are in common use today are:
GPT-4, Claude, Gemini - text generation models
Whisper - speech to text
DALL·E, Midjourney - image generation
It is important for product manager to choose the right model basis the kind of intelligence the product needs. Classifying customer complaints, generating travel itineraries or recommending top five packages are all different use cases and requires selecting the right model. Once you define the job, your ML team can pick or fine-tune the right model.
3. Framework & Tooling Layer
Imagine you have a Product catalog and want to build a feature that replies to customer reviews using AI. You’ve got a model like GPT-4. Now you need tools to build workflows, chain prompts, and track how well the AI performs. That’s this layer.
Some examples:
LangChain, LlamaIndex - chain prompts, structure reasoning
MLFlow - track experiments and model versions
PromptLayer, TruEra - monitor prompt quality, evaluate results
This layer controls reliability and iteration. You should be able to ask what happens when the model gives the wrong answer? Do we have logs of past outputs? And how will we evaluate performance?
It’s where your product moves from "one cool demo" to something robust and usable.
4. Application Layer
It’s the UI, the flow, the end-user experience. All the smart AI stuff only matters if it’s useful and usable up here.
Here’s where you answer:
How does the user trigger the AI feature?
Do we show the reasoning or just the output?
Can the user reject or correct the AI?
You can read more on how to design AI features here:
For example, if we’re building a tool for travel agents to create posters — the model might generate images, but we also have to think about:
How the agent inputs destination, prices and preferences
Whether they can edit the output
How we measure success (downloads? shares? bookings?)
How do we price the product so that we do not make losses.
The AI model isn’t the product. The product is the full experience around it.
5. The Data Layer
No model works without good data. Every AI model needs data - clean, relevant, and labeled.
When you are designing a feature you should ensure that you have reliable source of data which is available to train the model. Moreover, the data needs to be clean and useful. For example, if you want a model to recommend travel destinations based on past searches you need to collect, store, and protect that search data.
Where should you start?
If this feels like a lot, don’t worry. You’re not expected to master every layer. But you should know what each layer does, understand which layers your team is working on and speak the language of AI enough to collaborate well.
The goal isn’t to become an AI engineer. It’s to become the kind of product leader who understands what’s possible, knows how to ship it, and builds products users actually love. And once you do that, you’re not just “catching up.” You’re ahead of the curve.