Whether you’re building an experimental prototype for your personal use. Or creating an application to power an entire organization. There are key components of the AI technology stack you must get right.
This is how you build AI systems that do more than just generate answers. It’s how you solve real, meaningful problems.
Say, for instance, I’m building an AI-powered application. Its purpose is to help drug discovery researchers understand and analyze the latest scientific papers.
It might start with a new model I heard about. One that’s supposed to be better at highly complex tasks, like that of a PhD researcher.
The model is an important layer of the stack. But it’s just one piece of the puzzle.
The Complete AI Stack
To build a robust AI solution, you need to understand all its interconnected layers. Each choice impacts your system’s quality, speed, cost, and safety.
Here’s a visual breakdown of the stack.
graph TD;
A[1. Infrastructure <br> (Hardware: GPUs, Cloud, Local)] --> B[2. Models <br> (LLMs/SLMs, Open vs. Proprietary)];
B --> C[3. Data <br> (Sources, Pipelines, RAG, Vector DBs)];
C --> D[4. Orchestration <br> (Planning, Execution, Tool Calling, Review)];
D --> E[5. Application Layer <br> (Interfaces, Integrations)];
Let’s break down each layer.
1. Infrastructure
First, there’s the infrastructure that the model will run on. Not all Large Language Models (LLMs) can run on standard enterprise CPU-based servers. And not all are small enough to run on a laptop.
So, it matters what infrastructure you have access to and how you choose to deploy it.
When it comes to infrastructure, LLMs generally require AI-specific hardware, specifically GPUs. These can be deployed in one of three ways:
- On-Premise: Assuming you have the means and resources to buy this infrastructure yourself.
- Cloud: This allows you to rent capacity and scale it up or down as needed.
- Local: Usually means on your laptop, which can support smaller LLMs.
2. Models
The next layer is models. AI builders have plenty of choices here.
One dimension to consider is whether the model is open versus proprietary.
Another dimension is the model size. We have large language models (LLMs). We also have small language models (SLMs) that are lighter weight. These can fit on more lightweight hardware but might not have the same thinking capacity. Instead, they are often specialized for more specific tasks.
Finally, there’s specialization. Some models might perform better on things like reasoning, tool calling, or generating code. Others might have different language strengths.
There are over two million models in catalogs like Hugging Face. They can serve any mix of needs an AI builder might have.
3. Data
Next is the data layer. In our example, the whole point is to help scientists understand the latest papers. Models typically have a knowledge cutoff date.
So, if we want to talk about papers from the past three months, we have to provide the AI system with extra data.
This layer breaks up into a few different components:
- Data Sources: To supplement the model’s knowledge.
- Pipelines: To handle pre-processing and post-processing of that data.
- Vector Databases & Retrieval Systems: Also known as Retrieval-Augmented Generation (RAG).
Vector databases are where external data is vectorized into embeddings. These embeddings are saved so your model can retrieve that context more quickly. It augments the system with additional knowledge the base model does not have.
This is important because base models are usually trained on publicly available information. That information might not always be complete for your specific task.
4. Orchestration
Next is the orchestration layer. Building a complex AI system requires breaking the initial user input down into smaller tasks.
It’s more than just a single prompt and a single output. We want to break the user query into different parts. Help the AI plan how it’s going to tackle the problem. Figure out what data it needs. Then perform the summarization and create an answer. And maybe even review that answer.
These tasks can include:
- Thinking: Using the model’s reasoning ability to plan its approach.
- Execution: Where the model does tool calling or function calling.
- Reviewing: An LLM can provide its own critique of the initial responses and initiate feedback loops to improve them.
This layer is evolving very quickly. New protocols like MCP and new architectures are emerging for orchestrating complex tasks.
5. Application Layer
Finally, there is the application layer. At the end of the day, a user is using this tool. There has to be an interface that defines the inputs and outputs.
The most widely used AI systems follow a simple design of text-in and text-out. But as we use these tools more, other features become critical for usability.
The first factor is interfaces. The classic interface is text-in, text-out. But other modalities can be valuable too:
- Image
- Audio
- Numerical datasets
- Plenty of other custom data formats
In the interface, it’s also important to include the ability to do revisions or citations. When the user sees the output, they should have the ability to edit it or inquire further.
The second consideration is integrations. This works in two ways. First, allowing other tools the user uses to send inputs to the AI system. Second, taking the model outputs and automating how they get integrated into their day-to-day work.
[!NOTE] All together, these layers of the AI stack matter. From the hardware to the models, the data you use, how you orchestrate it, and the application’s usability. When we have a clear understanding of how they fit together, we can see what’s truly possible.
This allows us to make practical choices. To design AI systems that are reliable, effective, and aligned with our real-world needs.