Enterprise LLMs may seem like a magic wand, enabling your teams to process enormous amounts of proprietary and public data in seconds to inform intelligent business outputs. That’s the good news.
The not-so-good news is that you can’t just grab an off-the rack LLM and expect it to give you information tailored perfectly to your needs and in your brand voice. The results are, simply, too basic to be useful.
This is just one of several universal challenges teams encounter as they implement an enterprise LLM. What are these challenges and how can they be minimized?
What are the challenges of an enterprise LLM?
Accuracy and reliability
One of the biggest concerns around generative AI and enterprise LLMs is ensuring the data, and thus the outputs of the AI, is accurate and reliable. One way to do that is through prompt grounding. Grounding is when you provide specificity and context in the prompt, which results in much better outputs because the LLM bases its america phone number list responses on your real-world context.
Consider this example. A salesperson tells her AI assistant to schedule a meeting with Candace Customerman of Acme Corp. The system doesn’t know who Candace is, the topic of the meeting, what time zone she’s in, or what products she may have purchased or service issues she’s had in the past.
“In this case it’s just trying to guess, which can produce hallucinations and low-quality outputs that the salesperson would need to tailor by hand,” said David Egts, field CTO of public sector at Mulesoft. “But if you can ground it in real-world customer data, that’s where it’s helpful.”
The grounding Egts described is done with application program interfaces (APIs), which connect different software applications so they can communicate and share information with each other. These API connectors can help AI systems ground prompts in real-world, up-to-date information, even information that exists outside your CRM — like invoicing, inventory, and billing. Many companies, including Salesforce and Mulesoft, provide APIs.