LLMOps: Operationalizing Large Language Models in Production

LLMOps extends MLOps practices for the unique challenges of Large Language Models. Unlike traditional ML, LLMs require different approaches to testing, monitoring, and continuous improvement.

Key LLMOps Challenges

LLMs present distinct operational challenges: non-deterministic outputs, prompt sensitivity, context window management, and the difficulty of defining “correct” behavior. Traditional ML metrics like accuracy often don’t apply.

Implementing Prompt Versioning

from dataclasses import dataclass
from datetime import datetime
import hashlib
import json

@dataclass
class PromptVersion:
    name: str
    template: str
    version: str
    created_at: datetime
    metadata: dict

class PromptRegistry:
    def __init__(self, storage_client):
        self.storage = storage_client

    def register(self, name: str, template: str, metadata: dict = None) -> PromptVersion:
        """Register a new prompt version."""
        version_hash = hashlib.sha256(template.encode()).hexdigest()[:8]

        prompt_version = PromptVersion(
            name=name,
            template=template,
            version=version_hash,
            created_at=datetime.utcnow(),
            metadata=metadata or {}
        )

        self.storage.save(f"prompts/{name}/{version_hash}", prompt_version)
        return prompt_version

    def get_latest(self, name: str) -> PromptVersion:
        """Get the latest version of a prompt."""
        versions = self.storage.list(f"prompts/{name}/")
        return max(versions, key=lambda v: v.created_at)

    def get_version(self, name: str, version: str) -> PromptVersion:
        """Get a specific prompt version."""
        return self.storage.get(f"prompts/{name}/{version}")

Evaluation Framework

class LLMEvaluator:
    def __init__(self, judge_client):
        self.judge = judge_client

    async def evaluate_response(self, prompt: str, response: str, criteria: list[str]) -> dict:
        """Evaluate LLM response against criteria."""

        evaluation_prompt = f"""
        Evaluate this AI response against the criteria.

        Original prompt: {prompt}
        Response: {response}

        Criteria: {', '.join(criteria)}

        Score each criterion 1-5 and explain.
        """

        evaluation = await self.judge.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": evaluation_prompt}]
        )

        return self._parse_evaluation(evaluation.choices[0].message.content)

LLMOps is about building confidence that your AI system behaves correctly through systematic testing, monitoring, and iteration. Start with basic telemetry and expand based on observed failure modes.