Harvey.AI is following the playbook of Westlaw and Lexis by trying to establish itself as the go-to AI tool of choice for lawyers before they even become lawyers. I asked my university library to organize a Harvey demo so that we could think about joining the ranks of Stanford, UCLA, NYU, Notre Dame, WashU, Penn, UChicago, Boston University, Fordham, BYU, UGA, Villanova, Baylor, SMU, and Vanderbilt. (As reported by Above The Law) (https://abovethelaw.com/2025/10/harvey-snags-even-more-seats-in-the-t14).
This post is primarily based on a one-hour product demonstration given to us by a Harvey representative. To have a really well informed view on the product, I would want more hands-eye experience but there is surprisingly little information about what Harvey is actually offering online beyond the company’s own press releases. So, I thought my colleagues at other universities might find this assessment interesting.
TLDR
Meh, it’s OK, but law schools probably don’t need it and are probably only jumping on the bandwagon so that they can be part of the press release.
What is Harvey?
Harvey.AI is a legal-tech and professional services AI company whose flagship product is a generative AI assistant designed specifically for legal workflows used by law firms, in-house legal teams, and other professional services organizations. On its website, Harvey characterizes itself as “Professional Class AI” for leading professional service firms, emphasizing that its technology is domain-specific. In other words, it’s an AI system fine-tuned and optimized for legal and related professional work.
Use Cases and Contraindications
The first thing to understand about Harvey is that it is categorically not a legal research tool. Harvey essentially offers its clients a way of integrating generative AI into some routine drafting and analytical tasks that are quite common in legal practice.
Here are some common use scenarios:
If you have already identified the relevant case law and have a memo template to hand, Harvey AI can help you draft a legal research memo in double-quick time.
Alternatively, Harvey can help you review the key terms of a lengthy contract or almost any other synthesis or summarization task you could imagine.
Another good use case for the Harvey AI platform would be drafting an agreement or marking up the other side’s agreement in light of your own preferred templates. Harvey’s process for drafting from scratch seems directly analogous to vibe coding in software, but with a nice Microsoft Word integration.
You can also use Harvey for analysis and ideation (i.e., brainstorming). I can imagine coming to the end of a 3-month trial, throwing all the relevant documents into Harvey, and then launching into a discussion about closing argument strategy. Or, uploading a motion for summary judgment and the other side’s response, and then trying to anticipate the kinds of questions you might get from the bench.
The Harvey’s Value Proposition
You can already do almost all of this with ChatGPT, Gemini, Claude, and the like, subject to volume limitations on how many documents you upload. So, the natural question is, what value add does Harvey AI offer?
Fine tuning and model switching
One of the advantages claimed by Harvey is that rather than using foundation models like GPT directly, you would be engaging with custom versions of those model, fine-tuned on training data relevant to law and legal analysis. I could imagine that in some fields this would be a significant advantage, but I wonder how much of an advantage it is in the legal field given that most of that fine-tuning data is going to be public domain legal texts that are already well represented in the foundation models.
Another thing Harvey sees as a benefit is that they are not tied to any one model. They currently use three different fine-tuned foundation models, GPT, Gemini, and Claude, and they allocate tasks according to comparative advantage.
Security and confidentiality
By default, prompts and documents transmitted to a company like OpenAI may be used in training, will definitely be stored on OpenAI’s servers (at least for a while), and thus might be subject to discovery through appropriate legal processes. OpenAI has a setting where users can opt out of training that specifies that their data will only be retained for 30 days. This is probably good enough for many casual uses and even some mildly sensitive uses, but it’s obviously not enough for material that is subject to attorney-client privilege.
Accordingly, one of the key differentiators offered by Harvey AI is that the documents you upload and the prompts you write will not be accessible to Harvey or any third party, and that all of the information processing takes place in a secure Microsoft Azure environment with end-to-end encryption. This is probably the absolute minimum necessary to use LLMs for legal work. A large law firm could go one step further and actually host its own model in-house rather than relying on Microsoft. That extra layer of security might be required by some especially restrictive protective orders in litigation or by some especially sensitive clients. That sounds great, but I’m pretty sure I already get all that from Microsoft Copilot (although I would have to do a deep dive into the terms and conditions, Microsoft offers my university, to be sure).
Another nice feature of Harvey is that the client administrator can set permissions for individual users and for particular teams of users. This is critical in a corporate law environment where access to sensitive documents needs to be compartmentalized. It’s also critical if Harvey is being made available to students in a law school environment because students taking courses such as foundational Legal Writing and Research classes should probably not have access to Harvey AI.
Document Review (Retrieval-Augmented Generation)
Harvey AI has a good user interface for analyzing large volumes of documents. That is essentially an implementation of retrieval-augmented generation (RAG).
What’s RAG?
In very simple terms, RAG is an alternative to just answering a question through next-token prediction, relying on bulk context and whatever knowledge and understanding is latent in a foundation model. In a RAG process, the user query is translated into a document query. The document query identifies sections of documents that seem relevant to the query. Those sections are then collated and fed back into a general model which attempts to answer the question based on the specifically retrieved chunks of text. Platforms like ChatGPT are using a process like this any time you see them searching the web and providing links back to particular documents.
Harvey does RAG pretty well
RAG sounds like a great idea in theory. But whether it works in practice depends on how good the matching method is, which can vary a lot from context to context. In any RAG process, you will never know what relevant chunks of text were overlooked, and you won’t know whether the interpretive part of the model has drawn the appropriate inferences from the chunks it has retrieved unless you go back and check the original sources. One of the things I liked about the Harvey UX is that it made it easy to inspect the original document fragments and it had a clear process for checking off that these had actually been interrogated.
Example use cases would be looking for a change of control provisions in licensing agreements, as part of merger due diligence, or in document review for litigation. The Harvey representative we spoke to candidly admitted that the system performed really well in establishing a chronology, except in relation to emails. This makes sense, because an email thread contains lots of different dates all jumbled in together, but it is clearly a major limitation.
Prompting and training
Another value-add our representative stressed was prompting. Our representatives seem to be saying not only that Harvey would be running some thoughtfully-crafted prompts in the background, essentially running interference between user instructions and the models, but also that individual clients could do this for themselves. I can see why this might be an appealing feature to some people, but I’m not entirely convinced that making the steps in an analytical process obscure from the user is a good idea.
My Assessment
Generative AI as legal technology
Before we get into the specific pros and cons of Harvey, we need to consider the appropriate uses of generative AI as a legal technology more generally.
Many key deliverables in the legal field are in the form of text. But it’s relatively rare that the value of that text is entirely contained within the document itself. When a lawyer explains something to a client, they aren’t just helping their client understand something. They are also making a set of representations about the thought, diligence, and analysis that has gone into formulating that advice. Clients don’t just want text for its own sake, they want text you stand behind.
Accordingly, the most significant uses of generative AI in the legal field will be ones that accelerate a drafting-review or document-analysis process, as opposed to merely substituting for the underlying analysis.
Responsible use of generative AI in the legal field must be accompanied by either:
- strong validation mechanisms (such as a process for clicking through the footnotes to confirm that the document in question really says what the model represented),
- a knowing and well-informed acceptance of certain risks, or
- the kind of external validation that a lawyer who is already familiar with the underlying materials intrinsically provides.
The validity questions that need to be answered before deploying generative AI as a legal technology are not limited to the problem of hallucinations in the narrow sense of invented cases, citations, and quotations.
Harvey claims to do very well in dealing with hallucinations, but it’s important to situate this in the context that Harvey is not a legal research tool. The kinds of tasks that Harvey says that its product should be used for are exactly the kind of tasks where one would expect a much lower instance of hallucinations. Why? Because they are mostly summary or translation tasks where the model has specific documents or templates to draw from. Even so, I’m a bit skeptical that the rate of hallucinations is really as low as Harvey claims.
The value proposition for law firms
Depending on the cost, I can see that Harvey would be a very attractive proposition for law firms of all sizes. Most of what Harvey offers can be replicated through an enterprise agreement with one of the main AI providers. Harvey offers a turnkey solution and a good user interface. You can think of it as ChatGPT in a black turtleneck, but that’s no bad thing.
Is it worth it? That depends on the cost, and the cost of the alternatives.
The value proposition for law schools
There is no doubt that most of our students are already using generative AI. It seems appropriate that we begin training them to do so properly and responsibly at the earliest opportunity. That said, the availability of generative AI to students taking specific skills courses could easily undermine the development of those skills. Rather than simply making Harvey available to all students, it makes sense to exclude first-year students and perhaps some upper-level skills courses. But obviously, we would want students in our Advanced Legal Writing course (where we are teaching AI skills) to have access to this tool.
If we decide that we don’t want students in our clinics using generative AI, then one of the major selling points of Harvey disappears. Our students don’t need the robust confidentiality protection that Harvey offers.
If Harvey is offering commercially reasonable terms, I still think it is an attractive proposition. But its value in legal education seems to me to be really quite limited. Our students are not conducting massive document review exercises or working with in-house templates. Most of the things students would find compelling about using Harvey, they can already do with Microsoft Co-Pilot, ChatGPT, Gemini, and Claude.
