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Jan 10, 2026·12 min read

Integrating LLMs Into Production Systems

AI/MLBackend

I've shipped LLM-powered features in five production systems. Some worked brilliantly. One we rolled back after two weeks. Here's what I've learned.

Start With the Failure Modes

Map out what happens when the model gets it wrong. Wrong document label = human reviews it. Hallucinated chatbot answer = legal liability. The failure mode determines your guardrailing budget.

Prompt Engineering Is Software Engineering

Version control prompts. Test against 50-100 real inputs on every change. If accuracy drops, the change doesn't ship.

The Cost Trap

Route by complexity: cheap model for simple tasks, expensive model for hard ones. One project cut API costs by 60% with <1% accuracy loss.

Guardrails Are Non-Negotiable

Schema validation for structured output. Content filters for text generation. Confidence thresholds for human escalation.

When Not to Use LLMs

Deterministic rules? Use rules. Need 100% accuracy? Not LLMs. Regex or lookup table works? Use those. LLMs are for flexibility and language understanding.

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