LLMOps

LLM applications fail in new ways: drifting prompts, hallucination, rising token cost. LLMOps gives them the controls they need — evaluation suites, prompt and model versioning, monitoring, human-in-the-loop review and cost management.

• Data preparationCurated, chunked and indexed knowledge for retrieval.
• Prompt engineering and versioningPrompts treated as code, tested before release.
• Fine-tuning and orchestrationAdapting and chaining models where it helps.
• Evaluation and guardrailsAccuracy, safety and policy checks, automated.
• Deployment, monitoring and cost controlUsage, quality and spend tracked in production.