Most business systems users of AI won’t have a data science team, train their own model or even create sophisticated embeddings to manage their enterprise prompts. For most, an LLM is just a third party tool in their technology stack.
The last mile is where your system meets third party AI. The last mile is where everything breaks today, and tomorrow, it will break at the speed and complexity of AI.
Who cares how great the response was, if the system answered your customer 7 minutes after they abandoned the chat? Who cares if the LLM was spot-on, if its response was based on the first 50 characters of your FAQ.
Analysis of the 5 of the Last Mile Problems: An article from Ian Xiou in Towards Data Science
AI Risk Management Framework: A consensus resource from the US National Institute of Standards and Technology
The Ultimate AI Readiness Checklist for Marketing Ops and RevOps
A quick start guide to evaluate your organization’s AI readiness.
The Role of Synthetic Data in AI Testing
A look at the benefits of testing with synthetic data, a powerful alternative that is transforming how we validate AI systems.
Best Practices for Monitoring AI Systems Post-Deployment
A checklist of best practices to help you maintain and optimize your AI systems.
How to Mitigate AI Model Drift in Dynamic Environments
A look at types of AI model drift, its impact, and best practices for mitigating it in dynamic environments.
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