A Beginner’s Guide to the Generative AI Application Stack: Building with Purpose and Security

Generative AI is transforming how businesses solve problems and create user experiences, but if you’re new to it, the application stack can seem overwhelming. This simple guide breaks it down so that you can confidently plan and build your own AI-powered applications or APIs, even if you’re just getting started.

Step 1: Define Your Business Goal

Every application should begin with a clear purpose.

• What problem are you solving?
• What does success look like? Define measurable objectives.
• Who is the user, and what do they expect from the interaction?

Establish your metrics early so that you can track whether the model is delivering meaningful results.

Step 2: Plan the Infrastructure Layer

This is the foundation of your AI application. It includes:

• Compute: Choose hardware (cloud or on-prem) that supports training and inference.
• Storage: Prepare to store both model artifacts and user interaction data.
• Network: Ensure reliable, low-latency connections, especially important for real-time apps.
• Security: Secure the entire data lifecycle - training, storage, and inferencing.

This layer powers the rest of your stack. Prioritize scalability and compliance with privacy standards.

Step 3: Select and Integrate Your Model

You’ll need to decide which model (or models) to use:

• Foundation or fine-tuned LLMs?
• Consider latency: Do you need real-time or near-real-time responses?

Additional storage may be required to save user inputs and completions and/or store feedback for evaluation or future fine-tuning. You may also use model hubs or frameworks to manage and deploy your models more easily.

Step 4: Build the Interface Layer

This is how your users or systems will consume the model’s output. The common options are Web Applications and REST APIs. Think about who your users are: Humans via UI/UX and Systems via APIs.

Security is critical here. Authentication, rate limiting, and data isolation must be in place.

Step 5: Enable Real-Time Interaction and External Data Use

Depending on your use case, you may need:

• RAG (Retrieval-Augmented Generation) to fetch external data.
• User feedback collection to refine your model and align it with business goals.
• Monitoring tools to evaluate performance and track ongoing metrics.

This is where the application becomes intelligent, interactive, and continually improving.

Summary of the Generative AI Stack

Final Thought

Design your stack with intentionality. Each layer must support your goals, secure your data, and allow your users to interact meaningfully with your application. By understanding and applying this structure, you can move from idea to implementation confidently, even if you’re just starting your journey in generative AI.