Everyone is integrating LLMs into their products, but most implementations fall into the same traps. After shipping LLM-powered features across multiple products, here’s what actually matters.

Choosing Your API

Not all LLM APIs are created equal. Your choice should depend on latency requirements, cost constraints, and the complexity of reasoning needed. For most production use cases, you want a model that balances speed with accuracy — not the largest model available.

Here’s a basic integration pattern in Python:

import openai

client = openai.OpenAI()

def generate_summary(text: str, max_tokens: int = 150) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "Summarize the following text concisely."},
            {"role": "user", "content": text}
        ],
        max_tokens=max_tokens,
        temperature=0.3,
    )
    return response.choices[0].message.content

Prompt Design That Scales

Treat your prompts like functions. They should have clear inputs, defined behavior, and predictable outputs. Use structured output formats — JSON is your friend when you need to parse responses programmatically.

The Hallucination Problem

LLMs are confident liars. They will fabricate citations, invent statistics, and present speculation as fact with equal conviction. Every production system needs guardrails: output validation, source attribution, and fallback responses when confidence is low.

Production Checklist

  • Set reasonable max_tokens to control costs
  • Implement retry logic with exponential backoff
  • Cache frequent queries to reduce latency
  • Log all inputs and outputs for debugging
  • Monitor for prompt injection attempts
  • Rate-limit per user to prevent abuse

The gap between a demo and a production LLM feature is enormous. Budget accordingly.