Start Small: Practical AI System Design from Experience

I've spent the last few years building AI systems that actually work in production. Not the kind that look great in demos but fall apart when real users start pounding on them. The kind that handle real-world latency, orchestration challenges, and maintenance headaches.

Here's what I've learned: simpler, smaller, and well-observed systems scale better than complex, brittle ones. Let me walk you through what actually works when building LLM-powered applications.

Why Start Small Matters

When I first started working with LLMs, I made the classic mistake of over-engineering everything. I wanted to build the perfect system from day one. Big mistake.

The reality is that AI systems are unpredictable. Models drift, prompts change, and user behavior evolves. If you build something complex from the start, you'll spend more time debugging than building.

Instead, I've found that starting with a simple chain and gradually adding complexity works much better. You learn what actually matters to your users, and you can iterate quickly.

The Foundation: Chaining

Chaining is fundamental to AI applications. Instead of building one giant model or monolithic app, break down the task into smaller, sequential steps:

1. Process the raw query
2. Retrieve relevant context or documents
3. Construct a prompt with user input and context
4. Call the model to generate a response
5. Evaluate the response
6. Decide whether to return to the user or escalate to human

Each step is modular. You can test, cache, retry, and replace components independently. This structure improves debuggability and trust.

When I was building a customer support system, this approach saved us countless hours. We could isolate issues to specific steps rather than debugging the entire pipeline.

Orchestration: Connecting the Dots

An AI pipeline orchestrator lets you define how components talk to each other—retrievers, models, tools, evaluators. Think of it like Airflow for LLMs.

The orchestrator does two main jobs:

• Component Definition – What models, tools, retrievers, caches, and scoring systems you have
• Chaining – How the data flows between them

A typical AI orchestration flow follows this pattern:

1. User submits a query that passes through input guardrails
2. The system builds context (often through RAG)
3. This engages retrievers, tools, or databases
4. A prompt is constructed with the query and context
5. The LLM performs inference with this enriched prompt
6. The response is evaluated and scored for quality
7. Finally, the system decides whether to return the response to the user or escalate to a human

Common tools include LangChain, LlamaIndex, Langflow, Flowise, and Haystack. I've used most of these, and they all have their strengths. LangChain is great for rapid prototyping, while LlamaIndex excels at document processing.

If latency matters, run parallel chains. Don't serialize everything! I learned this the hard way when our response times were too slow for real-time chat.

From Modular to Complex: Know When to Scale

Here's what the architecture looks like at different stages of evolution:

Stage 1: Simple Response Retrieval

In this earliest stage, focus on building a basic query-to-response pipeline without persisting state. The system should accept a user query, apply simple validation, retrieve context if needed, and return a model-generated response.

No fancy tools, just the core flow. This is where you prove that your idea works and that users actually want it.

Stage 2: Introduce Write Actions Carefully

Adding write actions means you're letting the model update states or call external APIs. Handle with caution. This stage introduces:

• Confirmation steps before writes
• Sandboxed environments for testing
• Clear audit logs of model-initiated actions
• Rollback mechanisms when needed

I once had a system that accidentally sent emails to the wrong customers. It was a painful lesson in the importance of confirmation steps and audit logs.

Stage 3: Full-Fledged Architecture

At this advanced stage, your architecture includes:

• Sophisticated routing between specialized agents
• Multi-step reasoning with tool use
• State management across sessions
• Human-in-the-loop integration
• Comprehensive logging and monitoring
• A/B testing infrastructure for prompt variants

This is where you can really optimize for performance and user experience.

Drift Detection and Observability

Even with good design, models can drift. Prompt templates may change, and model versions may be silently updated. I've seen this happen with GPT-4—the March 2023 version was noticeably different from the June 2023 version.

Use tools like LangSmith, OpenTelemetry, or Prometheus to trace, log, and detect drift. I've found that having good observability is often more important than having the perfect architecture.

My Takeaways from Real Projects

• Start without orchestration—test your assumptions fast
• Add LangChain, Flowise, or LlamaIndex after proving value
• Use LangSmith or OpenTelemetry for observability
• Cache aggressively (responses, context, embeddings)
• Design your system with replaceable blocks

The key insight is that you don't need the perfect system from day one. Start simple, observe what works, and gradually add complexity where it matters.

Resources That Helped Me

These resources shaped my architectural thinking:

• "Designing Machine Learning Systems" by Chip Huyen
• "Machine Learning Specialization" by Andrew Ng
• "Full Stack Deep Learning" by Karpathy et al.

They helped me transition from quick hacks to scalable, explainable systems. Highly recommended if you're serious about ML architecture.

The Bottom Line

Building AI systems is hard, but it doesn't have to be overwhelming. Start small, observe everything, and add complexity only when you need it. Your future self will thank you.

The systems that work best in production aren't the ones with the most sophisticated architecture—they're the ones that are well-observed, well-tested, and built incrementally.

Happy building!