It’s Not Just the Model — It’s the Stack Around It
The rise of Generative AI has sparked a wave of innovation — and a wave of confusion.
Many teams think building a GenAI application is just about picking a model (GPT, Claude, LLaMA) and calling an API.
In reality, successful GenAI products require a multi-layered stack:
- Infrastructure
- Security
- Retrieval systems
- Prompt management
- Observability
- Feedback loops
If you're a CTO or engineering leader thinking “we’ll just add GPT to our app,” this article is your pre-build checklist for getting it right — at scale, securely, and sustainably.
Our POV: GenAI Apps Are Systems — Not Features
At ELYX, we’ve seen teams succeed when they stop treating GenAI as a plugin and start architecting it as a platform capability.
The GenAI stack needs to:
- Adapt to evolving use cases
- Comply with security and privacy constraints
- Allow experimentation without chaos
- Be observable, versioned, and governable
Let’s break down the layers that matter.
The GenAI Application Stack – Layer by Layer
1. Data & Retrieval Layer (RAG Backbone)
Purpose: Feed the model with your organization’s knowledge.
Includes:
- Vector database (e.g., Pinecone, Qdrant, Weaviate)
- Chunking + embedding pipeline (e.g., OpenAI, HuggingFace, Cohere)
- Metadata tagging, versioning, access control
- Hybrid search (semantic + keyword)
Why it matters: Without your own data, the model is just guessing.
2. Prompt Orchestration Layer
Purpose: Design, manage, and version the instructions that drive output.
Includes:
- Prompt templates (modular, role-aware, context-driven)
- Chaining frameworks (e.g., LangChain, Semantic Kernel)
- Retry, fallback, and error handling logic
- Dynamic variable injection (e.g., user profile, time, tone)
Why it matters: Prompt engineering is how you control model behavior — at runtime.
3. Model Access Layer
Purpose: Interact with the model(s) securely and efficiently.
Includes:
- Model gateways (e.g., OpenAI, Anthropic, Mistral APIs)
- Model routing or fallback logic (e.g., Claude → GPT fallback)
- JSON mode / function calling
- Token budget management
Why it matters: Different models excel at different tasks. You’ll need to abstract and switch intelligently.
4. Guardrails & Governance Layer
Purpose: Ensure safety, reliability, and compliance.
Includes:
- Output filters (toxicity, bias, policy violations)
- Guardrails (e.g., Rebuff, NeMo Guardrails)
- Rate limits, throttling
- Consent, logging, auditability
Why it matters: No model is perfect — guardrails protect both users and your brand.
5. Observability & Feedback Layer
Purpose: Measure, improve, and adapt the system.
Includes:
- Prompt + response logging
- Latency, accuracy, cost metrics
- Human feedback loop (thumbs up/down, comments)
- Offline evaluation datasets
Why it matters: You can’t improve what you don’t track — GenAI observability is essential for iteration.
6. DevOps & LLMOps Layer
Purpose: Deploy, version, and manage models + GenAI code safely.
Includes:
- Environment control (dev, test, prod)
- Prompt versioning + rollback
- Deployment orchestration (CI/CD for GenAI apps)
- A/B testing, canary deployments
Why it matters: GenAI introduces drift and unpredictability — treat it like a software system, not a script.
Real-World Example: Enterprise GenAI Knowledge Assistant
Use Case:
An insurance company wanted an assistant that answered internal queries about claims, policy terms, and legal documents.
What We Built:
- Retrieval layer using Weaviate + policy docs
- Prompt chaining for intent → retrieval → response
- Claude + GPT fallback routing
- Guardrails for confidential data + hallucination detection
- Observability via Trulens and custom dashboards
Outcome:
- 70%+ query success rate
- Zero PII exposure incidents
- Weekly improvement loop using feedback and evals
ELYX Perspective
At ELYX, we help enterprises:
- Architect GenAI stacks with modularity and governance in mind
- Choose the right mix of commercial + open-source components
- Balance developer flexibility with compliance and safety
- Establish LLMOps workflows that support continuous improvement
We don’t just wire up LLMs.
We design resilient GenAI systems that scale with your business and adapt to future models.
Final Thought: Don’t Build Fast. Build Right.
The difference between a demo and a deployable GenAI product is architecture.
Before you start building, ask:
- Can we control how the model behaves?
- Can we audit what it said last month?
- Can we retrain or re-route as our needs change?
If not, you’re not building an AI product.
You’re building a risk.
Ready to design your GenAI foundation the right way? Let’s architect it together.
