Building a Production LLM Pipeline: Lessons from the Trenches

Why Production LLMs Are Hard

Building an LLM demo is fun. Getting it into production reliably is a different beast. We shipped our first AI-powered feature — a code review assistant — to 50,000 users six months ago. Here’s what we learned.

Lesson 1: Latency is a First-Class Citizen

LLM inference is slow. Users expect sub-second responses for most UI interactions. We solved this by:

• Streaming responses via Server-Sent Events for a real-time feel
• Response caching for common queries using semantic similarity matching
• Tiered model routing — small, fast models for simple queries; larger models for complex reasoning

Lesson 2: Prompt Management is Engineering

Early on, prompts lived in code comments and Notion docs. As complexity grew, this became unmanageable. We built an internal prompt registry with versioning, A/B testing support, and automatic rollback.

Lesson 3: Evaluation is Non-Negotiable

You cannot ship AI improvements without a rigorous eval suite. We use a combination of:

• Human preference ratings on a golden dataset
• Automated LLM-as-judge for scalable quality assessment
• Regression tests tracking specific failure modes we’ve fixed

Lesson 4: Guard Rails, Guard Rails, Guard Rails

We had one incident where our assistant confidently provided incorrect information. Input validation, output filtering, and citation requirements dramatically reduced hallucination risks.

The Bottom Line

Production LLMs require the same engineering rigour as any other system. Treat prompts like code, invest in evals early, and always design for graceful degradation.