Taking an AI Feature to Production — Cost, Latency & Guardrails

Demos are easy; production AI is engineering. This is what a principal engineer checks before shipping an LLM feature — the stuff tutorials skip.

1. Cost — it scales with usage, not effort

• Count tokens. Input + output both bill. A 10-page document in every prompt is expensive at scale.
• Right-size the model. Use a small/cheap model for easy tasks (classification, extraction); reserve the big model for hard reasoning.
• Cache. Identical or similar prompts? Cache responses. Prompt caching cuts repeat costs dramatically.

2. Latency — big models are slow

• Stream responses so it feels fast.
• Run independent LLM calls in parallel, not in sequence.
• Show skeleton/loading states (how-to).

3. Reliability — LLMs are non-deterministic

# Always validate structured output — the model can drift from the format
try:
    data = json.loads(llm_response)
    validate(data, schema)
except (ValueError, SchemaError):
    retry_or_fallback()   # never trust raw LLM output blindly

4. Guardrails & safety

• Filter/validate user input (prompt-injection is real — users try to override your system prompt).
• Never let LLM output directly trigger dangerous actions without checks.
• Add "I don't know" paths; keep a human in the loop for high-stakes output.

5. Evals & monitoring — you can't improve what you don't measure

Build a small eval set: 20–50 example inputs with expected qualities. Run it whenever you change a prompt or model, so you catch regressions. Log real prompts/outputs (privacy-safely) and review failures weekly.