Why AI governance needs more than a token effort - Consultancy.uk

Chris Neilon of Lightouch Consulting warns that enterprises deploying large language models (LLMs) without a coherent token strategy risk runaway costs from verbose prompts and retrieval-augmented generation (RAG) pipelines. In an article published by Consultancy.uk, Neilon argues that token usage — the unit of input and output processed by LLMs — can spiral when users write overly detailed prompts or when RAG systems retrieve and feed large contexts into the model.

Neilon emphasises that many organisations overlook token budgets during AI adoption, focusing instead on model performance or integration complexity. He advises firms to implement prompt optimisation, where prompts are trimmed to essential context, and to monitor RAG pipeline costs by limiting retrieved document lengths. Without such measures, he says, operational expenses can quickly exceed expectations, particularly in customer-facing applications where high-volume queries are common.

The article positions token strategy as a core element of AI governance, alongside data privacy and model oversight. Neilon recommends that companies establish clear policies for prompt design and RAG architecture, and use analytics to track token consumption per use case. The warning reflects a growing recognition that LLM operational costs, while initially low, can become significant at scale if left unmanaged.