I'm starting a journey to understand how production-grade AI systems are designed, built, scaled, and maintained.
My goal isn't just to integrate an LLM API into an application. I want to understand the engineering decisions behind reliable AI products.
Let's start with a fundamental question:
What does it actually take to build an AI system?
1. An LLM API call is not a complete AI system
Calling an LLM API can generate a response, but production applications need much more:
- Authentication and authorization
- Data retrieval and context management
- Rate limiting and cost control
- Timeouts, retries, and error handling
- Evaluation and output validation
- Monitoring, security, and scalability
An LLM is one component of a larger software architecture.
2. Our first design problem
Imagine building an AI knowledge assistant for a company with 100,000 internal documents.
Employees should be able to ask questions such as:
"What is our enterprise refund policy?"
The system should retrieve relevant documents, generate an answer grounded in those documents, and provide citations.
It must also ensure employees cannot access information they aren't authorized to see.
3. A high-level architecture
Our initial design contains these components:
Client → Go API Backend → Retrieval Service → AI Orchestration → LLM Provider
The supporting infrastructure includes:
- PostgreSQL: users, document metadata, permissions, and application records.
- Vector search: retrieval of semantically relevant document chunks.
- Object storage: original uploaded documents.
- Message queue: asynchronous document processing.
- Background workers: text extraction, chunking, and embedding generation.
- Redis: selected caching and rate-limiting workloads.
These are initial technology choices, not universal requirements. The final architecture depends on workload, scale, cost, and operational constraints.
4. Where does RAG fit?
Retrieval-Augmented Generation (RAG) allows an application to retrieve relevant external information and provide it to an LLM as context.
A typical workflow looks like this:
- A user submits a question.
- The backend authenticates the user.
- The retrieval layer searches relevant, authorized document chunks.
- The orchestration layer builds a prompt containing the question and retrieved context.
- The LLM generates an answer.
- The backend validates the response and returns it with source references.
RAG can improve grounding, but it doesn't guarantee correctness. Poor retrieval, outdated documents, and misleading source material can still produce incorrect answers.
5. Production challenges worth thinking about
A working prototype is only the beginning.
Latency: How do we keep retrieval and generation fast enough for interactive use?
Cost: How do we control token consumption and avoid unnecessary model calls?
Security: How do we enforce document-level permissions before context reaches the model?
Reliability: What happens when the model provider times out or becomes unavailable?
Quality: How do we measure retrieval relevance and answer correctness instead of relying on a few manual tests?
These questions turn AI integration into system design.
6. What I'm exploring next
I'll investigate each component, implement practical examples, and document the architectural trade-offs as I learn.
The next topic is RAG: how document ingestion, chunking, embeddings, and retrieval work together—and why simply connecting an LLM to a database isn't enough.
This is the beginning of a hands-on AI system design series focused on practical backend engineering.
Question for other developers: When designing an AI knowledge assistant, would you prioritize retrieval accuracy, response latency, or cost first? Why?
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