🍔 Building a Generative AI App? Here’s Your LLM Burger Stack Explained!

By Abhishek Kumar — Azure AI Foundry Expert | Technical Architect

Imagine you’re crafting the perfect burger—each layer carefully chosen to build something delicious. Now, think of building a GenAI (Generative AI) application the same way.

Just like a burger, every layer in your AI app matters—from the juicy intelligence in the middle to the user-friendly top bun. Let’s break down each layer of the “LLM Burger Stack” that powers intelligent applications today.

🧱 1. Foundation Layer – The Bottom Bun

The strength beneath everything.

Every great app needs a solid base. This layer includes all the backend infrastructure that keeps your GenAI app running smoothly:

Cloud platforms (like Azure, AWS, or GCP)
Serverless compute (e.g., Azure Functions, AWS Lambda)
CI/CD pipelines for automation
Kubernetes or container orchestration
Logging, monitoring, and security best practices

Without this layer, everything else could fall apart—just like a burger without a bun.

🧠 2. Model Layer – The AI Brain

The patty that gives your app real power.

This is where the intelligence lives. It includes:

Foundation models like GPT-4, Claude, LLaMA, and Mistral
APIs for accessing these models
Fine-tuned or domain-specific variations
Token and context management to handle long inputs and outputs

The model layer is what gives your application the ability to understand, reason, and generate responses.

🧩 3. Data & Integration Layer – The Toppings with Flavor

Where your app gets context, freshness, and flavor.

Want your app to answer questions with up-to-date, accurate info? That’s where this layer comes in:

Retrieval-Augmented Generation (RAG)
Vector databases like Pinecone, Weaviate, or Chroma
Embedding models to store and search context
APIs, plugins, webhooks to connect with external tools and data

This layer enables real-time, context-rich AI responses.

🧠 4. Logic & Reasoning Layer – The Sauce and Spices

Where intelligence meets planning.

Here’s where things get interactive and thoughtful:

Agents (like CrewAI or AutoGen) for multi-step problem solving
LangChain, Semantic Kernel, or custom orchestrators
Tool usage and memory to simulate real-time thinking
Dynamic workflows, decision trees, and user-specific logic

This layer makes your app feel like it’s truly “thinking” through tasks.

💬 5. User Interface Layer – The Top Bun

The part users see, touch, and remember.

This is what people interact with:

Web and mobile UIs
Chatbots (in Slack, Teams, WhatsApp, etc.)
Voice assistants and browser extensions
Command-line tools or embedded chat windows

A beautiful top bun makes the whole burger appealing—your UI should be just as delightful!

👨‍🍳 Star Chefs Behind the Stack

Think of the following tools and platforms as the chefs who help you bring it all together:

OpenAI – Advanced language models
Hugging Face – Open-source model library
Pinecone / Chroma / Weaviate – Vector databases for fast retrieval
LangChain / CrewAI – Agents and orchestration
Zapier / Make.com – Integration with apps and tools
Vercel / Netlify – Frontend hosting

🍽️ Final Thoughts

Building a GenAI app isn’t just about plugging in a language model. It’s about carefully layering infrastructure, intelligence, integrations, logic, and interface—just like assembling the perfect burger.

Whether you’re a developer, architect, or product owner, knowing how these layers work together is the secret sauce to crafting a powerful AI experience.

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