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Trial deployment

As explained in How Large Language Models Work

A trial deployment is a limited release of a system conducted to evaluate its behavior and effects before broader adoption.

A trial deployment releases a system in a limited way before committing to broad adoption. It reveals whether the system helps, harms users, or creates costs that a simple process diagram hides. section 8.1

A careful trial deployment can:

  • Run the new system alongside the existing process for weeks or months while people continue using the established workflow. section 8.1
  • Compare outputs, identify discrepancies, address problems, and check whether performance degrades over time. section 8.1
  • Evaluate business effects such as customer retention before replacing human or outsourced support. section 8.1

Customer-support chatbot

A company can run an LLM chatbot beside its existing FAQs, email tickets, and support numbers, ignoring the chatbot's decisions at first. Comparing its answers with the established process can expose incorrect policy statements, liability, and hidden remediation work. section 8.1

Figure 8.1 When looking at the process diagram, it would seem like replacing FAQs, email tickets, and support numbers could be simplified and streamlined with an LLM-based chatbot. However, the folly of this view is that the process is incomplete. The potential errors and remediation processes required to ensure an LLM will perform accurately are hidden and create more complexity.
figure

The process diagram highlights how apparent chatbot simplification can conceal error handling and remediation work. section 8.1

How How Large Language Models Work covers it

How deep it goes
Explains parallel operation, comparison, remediation, and degradation monitoring, but stops short of specifying experimental metrics.
Examples
Uses an LLM customer-support chatbot, existing FAQs, email tickets, support numbers, and an errant airline policy statement.
What it assumes
Assumes an existing workflow or business process that can continue while the proposed system is observed.
What you can do after
Evaluate an AI deployment by comparing it with existing processes and identifying errors, harms, remediation needs, and performance decline.
How it connects
Connects trial deployment with machine learning systems, phantom deployments, customer support, liability, and adversarial risk assessment.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of How Large Language Models Work. The section links point to the parts of the book it draws on.