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Evaluating and tracing your AI App with Prompt Flow
An implementation with Python SDK and built-in Eval metrics
As Large Language Models (LLMs) continue to become more and more powerful in terms of reasoning capabilites, integrating these powerful models into applications has become both an opportunity and a challenge for developers and organizations alike.
However, typical GenAI applications are featured by a whole new set of components — prompts, vectorDB, LLMs, memory… — that need to be taken care of.
Managing the lifecycle of LLMs — from development to deployment and maintenance — has given rise to the concept of LLMops (Large Language Model Operations). Similar to MLOps, which streamlines machine learning operations, LLMops encompasses the tools, practices, and workflows necessary to harness the full potential of LLMs in real-world applications.
A critical component of the LLMops process is the evaluation step. Evaluation serves as the cornerstone for ensuring that LLM-powered applications perform as intended, meet quality standards, and deliver value to users. It involves systematically assessing the model’s outputs…
