The hidden technical debt in LLM apps

Interest in large language models (LLMs) has surged, leading to hidden technical debt that can harm scalability, maintainability, and cost-efficiency. Key areas of concern in LLM apps include prompt engineering, fragile pipelines, lack of observability and feedback, and cost unpredictability. Effective management strategies include investing in prompt management systems, implementing observability, automating evaluation and feedback loops, abstracting model providers, centralizing cost controls, and enforcing security and compliance. Using LLMOps tools can help mitigate these issues and build sustainable AI products.