Generative AI in Legal Education and Practice: Tools, Risks, and Future

General-purpose AI systems, often referred to as foundation models, are trained on massive datasets encompassing a wide array of information from the internet, books, and articles. Tools like ChatGPT, Claude, and Gemini fall into this category. They possess the ability to generate text, summarize complex documents, translate languages, and answer questions across a broad spectrum of topics. While these tools can sometimes produce citations or references when prompted, it is imperative to understand that these outputs may not always be accurate, complete, or sourced from reliable origins. Therefore, any generated content, including citations, must be rigorously verified against trusted sources. Treating AI-generated answers as definitive is ill-advised; critical evaluation and cross-referencing are essential for maintaining accuracy and integrity in legal work.

Fine-tuning involves taking a pre-trained foundation model and further training it on a specific dataset to adapt it for a particular domain, such as law. This process refines the model's existing parameters rather than building a new model from scratch. While the exact data used for fine-tuning is often proprietary, the goal is to enhance the model's proficiency with legal language and tasks. Retrieval-Augmented Generation (RAG), on the other hand, does not alter the foundation model itself. Instead, it incorporates an external retrieval component that searches a designated source for relevant information when a user submits a prompt. This retrieved content is then provided to the foundation model as part of its input, allowing it to generate a response informed by specific, external data. For legal AI, this external source is often a curated database of legal content. RAG aims to reduce hallucinations by grounding responses in authoritative sources, though it does not eliminate the risk entirely and can introduce new failure modes if irrelevant content is retrieved.

Several AI tools are gaining traction in the legal field. Apple AI, integrated into Apple devices, is set to enhance Siri with advanced AI capabilities, potentially offering a confidential AI option for law firms. Tools like CoCounsel, developed by Casetext and now part of Thomson Reuters, are designed as generative AI legal assistants. CoCounsel integrates legal research, guided workflows, and deep research capabilities, with ongoing developments including proprietary LLMs for legal use cases and agentic versions for more autonomous operation. Other notable legal AI tools include Protégé, Bloomberg Law, Descrybe, Harvey, Legora, Spellbook, Vincent, and VitalLaw AI. Each of these tools offers distinct features and functionalities tailored for legal professionals, though the underlying principles of AI generation and the need for verification remain consistent across the board.

The use of generative AI in legal practice introduces significant risks and necessitates a strong understanding of professional responsibilities. The potential for hallucinations, as discussed, can lead to severe consequences, as evidenced by cases where attorneys have been sanctioned for filing briefs containing fabricated information generated by AI. Attorneys have a duty of competence, which includes understanding the technology they employ. This means not only knowing how to use AI tools but also understanding their limitations and the imperative to verify all outputs. Client confidentiality is another critical concern, especially when using general-purpose AI tools. While some platforms are developing enhanced privacy features, the default settings and data handling policies of AI providers must be carefully considered. The ethical implications of AI in law are profound, requiring practitioners to exercise diligence, critical judgment, and a commitment to upholding the integrity of legal processes.