Prompt Engineering: Prompt engineering is the process of devising prompts that will produce the appropriate response from a GenAI model. Since the use of pretrained foundation models doesn’t provide much opportunity for customizing the data or context of those models, prompt engineering has been crucial for getting AI models to support certain use cases. You can’t control the data or the model itself, but you can control the prompts you feed into it.
In short, the GenAI ecosystem for the past year and a half netherlands whatsapp number data or so has been dominated by third-party foundation models, which were pretrained on generic sets of unstructured data, to address use cases that relied heavily on custom prompt engineering. In this world, vendors who built foundation models were essentially the gatekeepers, since their decisions about how the models worked and which data they trained on set the constraints surrounding how models could be used.
Looking to the future, this approach is poised to change in several key ways.
Custom Foundation Models
One of the biggest changes is the increasing availability of foundation models beyond those supplied by companies that specialize in generative AI services.
In addition to open-source models that have been released by companies like Meta and Google, we’re now seeing vendors like SAP developing their own foundation models. Crucially, these models will provide greater opportunity for enterprises to custom-model operations by injecting their own parameters to control the context in which the model operates. In some cases, they can also train or retrain models on custom data.