LLM Powered Application

LLM-powered applications represent a new software paradigm integrating large language models (LLMs) with traditional software to create AI agents or co-pilots for diverse tasks. Current research focuses on improving the efficiency and reliability of these applications, including optimizing workflows across multiple LLM requests, verifying their utility and alignment with user needs, and mitigating security risks like retrieval poisoning attacks. This field is significant because it addresses the practical challenges of deploying LLMs, impacting areas such as personalized recommendations, collaborative learning, and automated testing, while also raising crucial questions about transparency, human-agent alignment, and responsible AI development.