Enterprise Generative AI: Key Challenges and How to Solve Them

Large Language Models (LLMs) have revolutionized enterprise operations, but their adoption has been hindered by several challenges. These include concerns around application and return on investment, as well as issues with how LLMs handle and generate language. One major concern is the risk of hallucinations, where AI models can produce incorrect or misleading information due to ambiguity in natural language processing. Another challenge is ensuring compliance in non-compliant environments, as LLMs are essentially a 'black box' that can be vulnerable to theft and injection attacks. To address these issues, mature LLM implementations adopt a task-specific approach, focusing on specialized models trained for specific generative AI tasks. This helps improve accuracy, reliability, and grounding of the output while also incorporating verification mechanisms for better security and performance.