> TITLE: Lessons from Building Production AI Systems
> DATE: [2024-09-20]
> READ_TIME: 2 min read
> TAGS: #AI, #Engineering, #LLMs
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After spending the past year building AI systems in production, I’ve collected some hard-earned lessons that I wish I’d known earlier. Here’s what actually matters when moving from prototype to production.

The Eval Problem is the Whole Problem

The biggest challenge isn’t getting an LLM to do something impressive in a demo. It’s knowing whether your system is actually working at scale. Without rigorous evaluation, you’re flying blind.

What works:

  • Build evaluation datasets early, before you start optimizing
  • Track multiple metrics: not just accuracy, but latency, cost, and user satisfaction
  • Create regression tests for specific failure cases you discover
  • Use a combination of automated metrics and human review

Prompts are Code

Treat your prompts with the same rigor you’d treat any other code:

- Version control them
- Review changes carefully
- Test them systematically
- Document why they work

I’ve seen teams lose hours of optimization work because someone “improved” a prompt without understanding why the original version was structured that way.

The 80/20 of Retrieval

For RAG systems, the retrieval step determines your ceiling. A perfect LLM can’t fix bad context.

Focus areas:

  1. Chunking strategy: Semantic chunks beat fixed-size every time
  2. Metadata enrichment: Add context that helps with filtering
  3. Hybrid search: Combine dense and sparse retrieval
  4. Re-ranking: A small re-ranker model can dramatically improve relevance

Graceful Degradation

Your AI system will fail. Plan for it:

  • Set confidence thresholds and have fallbacks
  • Make it easy for users to get human help
  • Log everything so you can diagnose issues
  • Design UX that sets appropriate expectations

Cost Management

LLM costs can spiral quickly. Strategies that work:

  • Cache aggressively (same inputs = same outputs)
  • Use smaller models for simpler tasks
  • Batch requests when possible
  • Monitor usage by feature to find optimization opportunities

What’s Next

The field moves fast, but these fundamentals will serve you well. The teams that win are the ones that treat AI development with the same engineering discipline as any other software system.