have proven the hype remains strong well into its second year of hype. The technology is top of mind in every corner of the enterprise, with organizations placing it at the forefront of their product strategy.has quickly adopted some “write using AI” functionality, the value behind such capabilities is tactical and hasn’t yet transformed the organization at a strategic level.
However, even with larger limits, it’s impractical for companies to pack all of the contextual information needed for an answer into one prompt, both from a cost and latency perspective. For example, an LLM cannot take 200 calls and 2,000 emails with a customer into account in a single prompt; that would take hours to generate the response.Many organizations train LLMs on their business-specific data.
To complement intent mapping, teams need effective tools to address the main intents, while ensuring that all intents are handled well.To answer domain-specific questions, data must be preprocessed and “tagged” to be identified. For example, when a user searches for a customer’s “business goals,” the system must quickly identify such goals. Since the number of such potential goals is huge, it will take a long time to look for all of them.
Effective indexing requires an in-depth understanding of the domain to identify cues and intent in the data, even when they are mentioned only implicitly.In the context of business questions, text-based answers can be hard to consume.