Building real software with LLMs is fundamentally different from chatting with a bot. In production applications, you need **predictable schema outputs**, **low perceived latency through streaming**, and **strict guardrails** to prevent prompt injections.
1. Type-Safe Structured Outputs
Never rely on standard natural text parsing with regex when extracting data. Modern foundation models support native JSON schemas and strict function calling.
Python / OpenAI SDK
from pydantic import BaseModel
from openai import OpenAI
class CodeReviewResult(BaseModel):
summary: str
severity: str # 'low' | 'medium' | 'critical'
suggested_fix: str
performance_impact: bool
client = OpenAI()
completion = client.beta.chat.completions.parse(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": "You are an expert code review engine."},
{"role": "user", "content": "Review this function: async def get_data(): ..."}
],
response_format=CodeReviewResult,
)
# result is 100% typed and guaranteed to match schema
review: CodeReviewResult = completion.choices[0].message.parsed
print(f"Severity: {review.severity}, Fix: {review.suggested_fix}")2. Perceived Latency with Streaming (SSE)
LLMs generate responses token-by-token. Instead of making your users wait 4-8 seconds for a complete response, stream tokens directly to the browser via Server-Sent Events (SSE) or WebSockets.
User Experience Impact: Streaming reduces the perceived wait time (Time to First Token) from 5 seconds down to ~300 milliseconds.