60% of LLM Apps Fail Without Iterative Evaluation: Here's Why

In the development of Large Language Model (LLM) applications, iterative evaluation is crucial for success. A study shows that 60% of LLM applications fail to meet expectations without this process. The iterative approach involves selecting relevant metrics, gathering real-world data, and continuously evaluating the application’s performance. This process, often automated through CI/CE/CD (Continuous Integration/Continuous Evaluation/Continuous Deployment), is now known as LLMOps. Tools like PromptFlow and Vertex AI Studio facilitate this automation. However, evaluating LLMs and LLM-based applications differs; while LLMs undergo standard benchmarks, applications like RAG systems require domain-specific, real-world datasets for accurate performance assessment. Ethical considerations are also vital, ensuring transparency, fairness, and accountability in applications, especially in sensitive areas like healthcare. Additionally, the cost of LLMs varies significantly, with models like OpenAI’s o1 costing $15.00 per 1M input tokens compared to $2.50 for GPT-4o.

Source: towardsdatascience.com