  The legal profession necessitates a multidimensional approach that involves
synthesizing an in-depth comprehension of a legal issue with insightful
commentary based on personal experience, combined with a comprehensive
understanding of pertinent legislation, regulation, and case law, in order to
deliver an informed legal solution. The present offering with generative AI
presents major obstacles in replicating this, as current models struggle to
integrate and navigate such a complex interplay of understanding, experience,
and fact-checking procedures. It is noteworthy that where generative AI outputs
understanding and experience, which reflect the aggregate of various subjective
views on similar topics, this often deflects the model's attention from the
crucial legal facts, thereby resulting in hallucination. Hence, this paper
delves into the feasibility of three independent LLMs, each focused on
understanding, experience, and facts, synthesising as one single ensemble model
to effectively counteract the current challenges posed by the existing
monolithic generative AI models. We introduce an idea of mutli-length
tokenisation to protect key information assets like common law judgements, and
finally we interrogate the most advanced publicly available models for legal
hallucination, with some interesting results.
