LLMOps extends MLOps for the unique challenges of large language models. Evaluation, monitoring, and cost management require specialized approaches.
Evaluation Challenges
Traditional metrics don't work:
- No ground truth labels
- Subjective quality
- Multiple valid outputs
Evaluation Approaches
- Human evaluation at scale
- LLM-as-judge patterns
- Task-specific metrics
- A/B testing
Prompt Management
Version control prompts:
name: summarization-v2
model: gpt-4
temperature: 0.3
prompt: |
Summarize the following text in 3 bullet points:
{text}
Cost Optimization
LLM costs add up:
- Cache common queries
- Use smaller models when possible
- Optimize prompt length
- Batch requests
Monitoring
Track:
- Latency percentiles
- Token usage
- Error rates
- User feedback
- Content filter triggers