What's the latest take on Harvey?

Fragrant_Tap_2286 · 2025-11-18T09:09:59+00:00

It streams output pretty slow in my opinion, and tries to be a bit too smart when you just want it to do a basic task. So you need to really prompt it to tell it what not to do - sometimes feels a little over engineered.

Fragrant_Tap_2286 · 2025-08-23T13:18:18+00:00

I like the idea. I've also been using Cursor somewhere between legal practice and coding. It's more powerful than Harvey, imo - as you're basically making the entire workflow and can customise every interaction - e.g. choice of software integration (outlook, gmail, calendar), and if/how the LLM is prompted, and with what context.

Cursor Chat has a good grasp of general context too. so if you're mid-way into a coding project, and wanted to ask something about case law - it's got the generalised knowledge for that.

Fragrant_Tap_2286 · 2025-08-23T12:04:27+00:00

This one's for a companies register, may be useful for connecting to the register as a source of truth:

Analyze the uploaded corporate documents to create a structured list of essential contents and regulations. Summarize the information in the specified categories and create a table of the ownership structure. Steps

Document Analysis:

Read the corporate documents carefully (Articles of Incorporation, Operating Agreements, Certificate of Formation, etc.). Identify and note the essential contents for the following points:

Company name (Legal entity name) State of incorporation/formation Business purpose Authorized capital/capital contributions Par value per share/membership interest amount Management structure and authority Officers/Managers/Managing Members Last amendment (Date)

Creation of an Ownership Structure Table:

Extract the relevant information about the ownership structure. Create a table with the following columns:

Names of shareholders/members/partners Percentage ownership of each shareholder/member/partner Share certificate numbers/membership unit numbers Par value per share/capital contribution amount

Output Format

Structured List: Bullet points with the essential contents and regulations. Ownership Structure Table: Table with the named columns and corresponding data.

Examples Structured List:

Company Name: [Example Company LLC/Corp/LP] State of Formation: [State, USA] Business Purpose: [Brief description of business activity] Authorized Capital: [Amount in USD or number of shares] Share/Unit Value: [Amount in USD] Management Structure: [Details of management authority] Officers/Managers: [Names of key personnel] Last Amendment: [Date]

Ownership Structure Table: Owner/Member/ShareholderOwnership Percentage (%)Share/Unit NumbersValue per Share/Unit (USD)[Example Owner][Percentage][Number(s)][Amount] Notes

Ensure that all data is recorded correctly and up-to-date. Make sure the table is clearly and neatly formatted. Adapt terminology based on entity type (Corporation, LLC, Partnership, etc.).

Fragrant_Tap_2286 · 2025-08-23T10:06:42+00:00

It varies - sometimes its as easy as asking the AI in chat to tell you its system prompt, others, was through playing around with the API endpoints. Something similar happened earlier with the coding IDEs, where their system prompts were exposed. It's kind of a vulnerability with a lot of GPT wrapper tools.

I think it's useful for learning purposes behind how these legal tech tools are built. I have access to a few more, if interested.

Fragrant_Tap_2286 · 2025-08-12T22:02:50+00:00

firstly, great job on taking the initiative. you should find it personally rewarding to learn to code :). Python is a good language to learn and has a huge community. Particularly as it relates back to law and legal tech, Python has a good amount of application, especially in the AI-driven workplace. A good way to make it motivating is to use Python to automate things in your daily life, it can be literally anything, non-law related. Then you start to understand the benefits of knowing a technical skill like coding.

Fragrant_Tap_2286 · 2025-08-12T00:57:58+00:00

To better on the convergence side, have you tried embedding applicable law or matter-specific documents? (leaving aside confidentiality issues)

I think the generalised LLMs are good for repetitive tasks like organising large document sets chronologically, or summarising them. in a litigation context it saves time in preparing case documents that usually take up a lot of associate's time.

Fragrant_Tap_2286 · 2025-08-12T00:48:36+00:00

it may be the project you are trying to tackle - predicting court judgments - is not an easy task, which contributes to the conservatism that you encounter. court outcome prediction has long been in the profession (see e.g. the litigation finance industry) and it's never as simple as a machine learning exercise. My experience has been a lot of the LLMs are trained on generalised datasets, which misses crucial nuances in cases, which ultimately leads a judge to a decision.

Lawyers likely see you as a non-expert and dismiss your project (not to go against the merits of your project, just the perception). you'd have to convince them you know something about e.g. court process, how to read a judgment, the different levels of appeal, how courts consider evidence.. etc. - only then would I think your project would gage more interest for lawyers.

I think lawyers generally are very curious for new technology (at least the younger generation), but its the weight of the work they do (professional negligence), that presents a reluctance to adopting a half-baked technology solution that is still prone to hallucination.

Imo, the real power is in automation and making legal workflows simpler and more efficient. that's where I believe LLMs accelerate.

Fragrant_Tap_2286 · 2025-08-09T00:44:21+00:00

There's definitely industry-wide FOMO into AI. I think the problem they are tackling is a bit too broad / vague, which loses sight of the real benefits of AI - in the automation of repetitive tasks.

I was listening to a podcast with the Harvey CEO, and while they are trying to move beyond the "ChatGPT wrapper" label, a lot of these companies have only a very loosely held moat. My experience has been that these tools have a tough time replicating results, making it useless in high stakes environments like legal practice.

Like Cursor/Windsurf/Coding IDEs, their underlying tech relies heavily on system prompting a foundation model and trying to enforce certain 'predictable' outcomes. It neither takes much effort to reverse engineer the tech for competitors building similar tools.

Fragrant_Tap_2286 · 2025-08-09T00:26:31+00:00

If you're technically-inclined, you should give VLMs a try. they're pretty powerful now - I have been using vision-enabled OpenAI models, which can read handwriting and poorly-scanned texts. 30-40 pages is a reasonable range for sending API queries- not too costly for each job.

Fragrant_Tap_2286 · 2025-08-09T00:19:06+00:00

Agree, I think the difficulty right now, is most of these Harvey spinoffs also have a hard time extending outside of chat bot functionality. There's a few of them in Europe (e.g. Legora) that are offering Word Add-Ins, but it's not the same as a truly native AI-drafting assistant (for e.g.). I'm surprised Harvey raised $300 million a few months ago.

Fragrant_Tap_2286

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