Best practices

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Discover capabilities

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Set explicit boundaries

• Set connect, read, and total deadlines for every request.
• Bound input bytes, message count, tool schema size, image dimensions, audio size, and output tokens.
• Limit tool-loop iterations and concurrent model calls per user.
• Use server-side cancellation when the caller disconnects.
• Reject unknown user-selected models unless they are in an allowlist built from the current catalog.

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Retry selectively

curl --connect-timeout 10 --max-time 90 \
  --retry 3 --retry-all-errors --retry-delay 1 \
  https://api.yelu.ai/v1/models \
  -H "Authorization: Bearer $YELU_API_KEY"

const controller = new AbortController();
const timeout = setTimeout(() => controller.abort(), 90_000);
try {
  const response = await fetch('https://api.yelu.ai/v1/models', {
    headers: { Authorization: `Bearer ${process.env.YELU_API_KEY}` },
    signal: controller.signal,
  });
  if (!response.ok) throw new Error(await response.text());
  console.log(await response.json());
} finally {
  clearTimeout(timeout);
}

import os
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

session = requests.Session()
session.mount("https://", HTTPAdapter(max_retries=Retry(
    total=3,
    backoff_factor=0.5,
    status_forcelist=[429, 500, 502, 503, 504],
    allowed_methods=["GET"],
)))
response = session.get(
    "https://api.yelu.ai/v1/models",
    headers={"Authorization": f"Bearer {os.environ['YELU_API_KEY']}"},
    timeout=(10, 30),
)
response.raise_for_status()
print(response.json())

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Protect data and keys

• Keep Yelu keys in server-side secret storage and use one key per environment/workload.
• Redact authorization, prompts, tool arguments, files, and model output from default logs.
• Classify data before sending it to any AI model and enforce your retention and residency requirements.
• Treat model output, URL input, and function arguments as untrusted.
• Require deterministic authorization for every tool action.

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Make outputs dependable

• Use structured output for machine-consumed data and validate it locally.
• Use low randomness for extraction, classification, and workflow control.
• Check finish status before consuming output; length and content filtering can produce incomplete results.
• Maintain versioned evaluation cases for prompts, schemas, model changes, and fallback behavior.
• Make fallback models explicit because quality, safety, latency, and output shape can differ.

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Observe the system

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Control cost

• Choose the smallest model that passes your evaluation threshold.
• Trim conversation history and retrieve only relevant context.
• Cap output, image count, dimensions, and audio duration.
• Deduplicate safe, deterministic work at the application layer.
• Attribute usage with separate keys and dashboard monitoring.