⚙️ Production Deployment

import ContributionButtons from ’../../../../components/ContributionButtons.astro’; import UsageTracker from ’../../../../components/UsageTracker.astro’; import AuthorshipBadge from ’../../../../components/AuthorshipBadge.astro’; import GreaterGoodBadge from ’../../../../components/GreaterGoodBadge.astro’; import CookbookAsCode from ’../../../../components/CookbookAsCode.astro’; import LearningPath from ’../../../../components/LearningPath.astro’; import InteractiveQuiz from ’../../../../components/InteractiveQuiz.astro’; import UnderstandingButton from ’../../../../components/UnderstandingButton.astro’;

🌳 Forest-Level Concept

Label: Hardening AI Systems for the Real World

Deploying AI is not just about “deploying a model.” It’s about deploying a Reliable Software System around an Unreliable Core. Production deployment focuses on deterministic safeguards, cost control, and pervasive observability.

The Production Pillars

1. Evaluations (Evals): Automated testing for prompt quality.
2. Guardrails: Runtime validation of inputs and outputs.
3. Observability: Tracing LLM calls, latency, and token costs.
4. Governance: Rate limiting and security.

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🌿 Growing: Infrastructure & CI/CD

1. LLM-as-a-Judge Evaluation

In production, you can’t manually check every output. You use a stronger model (like GPT-4o) to grade the outputs of a faster model (like GPT-4o-mini).

scripts/eval.py

def evaluate_response(query, response):
    eval_prompt = f"""
    Grade the following AI response for factual accuracy.
    Query: {query}
    Response: {response}
    Grade (1-10):
    """
    # Call judge model
    score = judge_model.generate(eval_prompt)
    return int(score)

2. Guardrails with Pydantic

Never let raw LLM strings hit your database. Force structured outputs.

from pydantic import BaseModel, Field
from typing import List

class SearchResponse(BaseModel):
    summary: str = Field(description="A brief summary of the findings")
    sources: List[str] = Field(description="List of URLs or citations used")
    confidence_score: float = Field(ge=0, le=1)

# Use with instructor or Vercel AI SDK
structured_output = ai_client.chat.completions.create(
    model="gpt-4o",
    response_model=SearchResponse,
    messages=[{"role": "user", "content": "..."}]
)

---

🌳 Forest: Monitoring & Safety

Distributed Tracing

Each AI request travels through multiple steps (retrieval, reasoning, tool use). You must track the “Trace ID” across all steps to find where it fails.

Metric	Threshold	Action
API Latency	> 5s	Alert Engineering
Token Cost/User	> $1.00/hr	Rate Limit
PII Detected	> 0	Block Output
Hallucination Score	> 20%	Flag for Review

🎓 Knowledge Check

<InteractiveQuiz quizId=“prod-guardrails” question=“What is the primary purpose of ‘Guardrails’ in a production AI environment?” options={[ “To make the model run faster on local hardware”, “To prevent toxic, malformed, or out-of-bounds outputs from reaching users”, “To automatically generate new training data”, “To encrypt the LLM weights” ]} correctAnswer={1} explanation=“Guardrails act as a safety and quality layer that validates both inputs and outputs at runtime.” />