The rise of large language models (LLMs) has transformed the way we think about information retrieval. Tools such as ChatGPT, Gemini and Perplexity can produce fluent and persuasive answers to almost any query. Yet beneath the polished prose lies a serious risk for legal practice: plausibility is not the same as reliability.
In law, where outcomes affect livelihoods, reputations, and rights, “good enough” answers are not good enough. Precision, transparency and verifiability remain non- negotiable.
1. The limits of plausible text
LLMs work by predicting the most likely next word. The same algorithm that generates a recipe for chocolate cake is applied when asked about the elements of a negligence claim. There is no legal reasoning “baked in.”
The result is answers that may look authoritative but rely on secondary commentary, incorrect jurisdictions, or in some cases fabricated citations. Courts in Australia and abroad have already sanctioned practitioners who filed submissions containing fake cases.
What is persuasive at first glance can become professionally dangerous when tested against the rigour of legal standards.
2. Embedding legal method into AI
MiAI Law was designed to address this gap. Its foundation is not probability but first principles. Every case in our database is analysed systematically: the facts, issues, arguments, reasoning, findings and rules are extracted and coded.
When a user poses a query, MiAI runs through a step-by-step reasoning plan — often 18 to 20 steps for complex questions. Each retrieval is filtered, irrelevant cases are discarded, and the relevant authorities are footnoted to pinpoint citations.
At the end of each research report, MiAI reveals its reasoning path: the questions it asked, the material retrieved, and the cases relied upon. This transparency is what we describe as evidence-grade research.
3. From information to structured proof
Legal practice is not merely about collecting documents. It is about applying structured legal tests to prove each element of a cause of action.
In traditional research, a barrister might reduce thousands of keyword hits to a manageable 80–100 cases and then work through each to identify principles. MiAI compresses that grind by automating the sifting, but the structure remains intact.
The lawyer still applies judgment: deciding which arguments to run, which to abandon, and how to frame strategy. MiAI accelerates the search for authority; it does not replace the advocate.
4. Guardrails against error
No AI system can promise perfection. By nature, LLMs will generate an answer even if the data does not exist. MiAI addresses this by building in constraints. If the system cannot support a conclusion, it says so.
Every proposition in a report is footnoted, hyperlinked to source, and cross-referenced. Users can click through to verify paragraphs in legislation or judgments. This reduces the risk of being led down irrelevant tangents or, worse, relying on fabricated cases.
The contrast with public tools is stark. Where they risk wasting time on irrelevancies, MiAI ensures that lawyers start with a solid, verifiable foundation.
5. The “80% is enough” fallacy
Some practitioners have suggested that if an AI output is “80% correct,” that is sufficient. I strongly disagree. A contract drafted on the basis of a handful of generic prompts is not 80% fit for purpose. In my assessment, such outputs are closer to 40% accurate, and that margin invites professional negligence.
AI should reduce the workload, not remove responsibility. Lawyers must still check, verify, and apply experience. To abdicate that responsibility is to undermine the profession’s role as guardian of justice.
6. Reliability as the benchmark
Benchmarking exercises against existing platforms confirm the difference. While some general models outperformed traditional databases on superficial tests, MiAI’s strength lies not in speed alone but in the auditability of its reasoning.
This is what makes legal AI trustworthy. The profession must hold to a higher bar than plausibility. We must demand systems that are transparent, jurisdiction-specific, and anchored in primary authority.
7. Conclusion
The future of legal research is not about producing sentences that “sound right.” It is about delivering reliable, evidence-grade answers that can withstand judicial and professional scrutiny.
MiAI Law does not claim to replace lawyers. It aims to empower them: to move faster without sacrificing rigour, to think more strategically with confidence in their sources, and to ensure that every answer is verifiable.
In law, speed without reliability is not an upgrade. It is a liability. The true promise of AI lies in bridging that gap — from plausible language to legal reliability.
