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GPT-5 for Developers: Practical Patterns and Example Code
June 30, 2025•8 min read•FromYou AI Team
What Matters in Production
Building with GPT-5 goes beyond a single API call. Production apps need streaming UX, robust error handling, prompt versioning, evals, analytics, and cost controls. Below are field-tested patterns to move fast without breaking reliability.
Architecture Patterns
- • Proxy requests via your server to protect API keys and enforce rate limits.
- • Use background jobs for long-running tasks; stream partial results to the UI.
- • Store prompts and completions with versions for reproducibility and audits.
- • Implement content safety gating where applicable.
Tool Use and Function Calling
Function calling with GPT-5
const completion = await openai.chat.completions.create({
model: "gpt-5",
messages: [{ role: "user", content: "Book a table for 2 at 7pm" }],
tools: [
{
type: "function",
function: {
name: "reserve_table",
parameters: {
type: "object",
properties: { time: { type: "string" }, size: { type: "number" } },
required: ["time", "size"],
},
},
},
],
});Validate tool arguments server-side and handle idempotency to prevent duplicate side effects.
Observability and Evals
- • Log latency, token usage, and outcome metrics per route.
- • Adopt eval suites for core prompts and regressions.
- • Capture user feedback and close the loop with prompt updates.
Keep Building
Next, read our GPT-5 Integration Guide, compare GPT-5 vs GPT-4, and check API pricing.