LLM Ops
Operate large language models at scale with prompt management, evaluation, and monitoring.
How it works
Generates LLM application infrastructure: prompt management, response caching, token cost tracking, rate limiting across providers, fallback chains, and evaluation pipelines. Handles prompt versioning, A/B testing prompts, and monitoring for hallucinations and quality regression.
Example prompts
“Build a prompt management system with versioning and A/B testing”
“Create an LLM gateway with caching, rate limiting, and fallback”
“Set up evaluation pipelines for LLM response quality”
Real-world example
A company spending $15K/month on LLM APIs built a gateway with semantic caching (40% cache hit rate), automatic fallback between providers, token budget alerts, and prompt A/B testing — reducing costs to $8K while improving response quality through systematic evaluation.
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