Start Small: Practical AI System Design from Experience

🚀 Why You Should Start Small with AI Architectures

Designing an AI system is often overhyped and misunderstood. After working on model-driven systems and facing real-world latency, orchestration, and maintenance challenges, I've learned that simpler, smaller, and well-observed systems scale better than complex, brittle ones.

Let me walk you through what works in practice when building an LLM-powered application—and why chaining, orchestration, and observability must be your guiding principles.

---

🔁 Step 1: Chaining — The Foundation

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 + context
4. Call the model (LLM) to generate a response
5. Evaluate the response
6. Decide 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.

📌 These steps visualized in orchestrators like LangChain's AgentExecutor trace make debugging and pattern recognition much easier.

---

🤖 Step 2: Orchestration — Connect 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.

At a high level, the orchestrator does two 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: LangChain, LlamaIndex, Langflow, Flowise, Haystack

If latency matters, run parallel chains (e.g., PII removal + routing). Don't serialize everything!

---

🧱 Step 3: 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.

Stage 2: Introduce Write Actions Carefully

Adding write actions means you're letting the model update states or call external APIs (like sending emails or updating databases). 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

Stage 3: Final 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

---

🧪 Step 4: Drift Detection & Observability

Even with good design, models can drift:

• Prompt templates may change
• Model versions may be silently updated (e.g., GPT-4 in March vs June 2023 had notable differences)

🧠 Research: Chen et al. (2023) arxiv link, Voiceflow Blog

Use tools like LangSmith, OpenTelemetry, or Prometheus to trace, log, and detect drift.

---

💡 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.

---

📚 Resources That Helped Me Build AI Systems

These books/courses 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.

---

Stay tuned for more practical system blogs.

Happy shipping! 🚀