Beyond the model
Generative AI systems are not defined only by the underlying model. The surrounding application, data sources, retrieval mechanisms, prompts, interfaces, and evaluation process all influence how useful the final system becomes.
Grounding AI with information
Connecting generative models with trusted information can make responses more relevant to a particular domain or organization. Retrieval-based approaches can help systems work with information that is not contained directly within the model.
Practical applications
Generative AI can support document analysis, knowledge retrieval, content generation, conversational interfaces, summarization, and other workflows where language and information are central to the task.
Reliability and evaluation
Useful generative systems require more than impressive outputs. Evaluation, context selection, system design, and continuous refinement are important for understanding when a system performs well and where it needs improvement.