You would also need to load an enormous amount of precedential case law, at least in the US and other common law jurisdictions. Synthesizing case law into rules of law applicable to a specific case requires complex analysis that is frequently sensitive to details of the factual context, where LLMs' lack of common sense can lead it to make false conclusions, particularly in situations where the available, on-point case law is thin on the ground and as a result directly analogous cases are not available.
I don't see the utility at the current performance level of LLMs, though, as the OP article seems to confirm. LLMs may excel in restating or summarizing black letter or well-established law under narrow circumstances, but that's a vanishingly small percentage of the actual work involved in practicing law. Most cases are unremarkable, and the lawyers and judges involved do not need to conduct any research that would require something like consulting an AI assistant to resolve all the important questions. It's just routine, there's nothing special about any given DUI case, for example. Where actual research is required, the question is typically extremely nuanced, and that is precisely where LLMs tend to struggle the most to produce useful outputs. LLMs are also unlikely to identify such issues, because they are issues for which sufficient precedent does not exist and therefore the LLM will by definition have to engage in extrapolational, creative analysis rather than simply reproducing ideas or language from its training set.