Workflow Options: From Prompts to Presets

Every lawyer who has ever stared at a blank prompt box knows the feeling. AI promised to change how legal work gets done — but a single chatbox isn't a workflow. The legal AI world is splitting in two: enterprise ecosystems building choreographed plugin infrastructure, and vertical tools purpose-built for specific practice areas. The real question isn't which AI is smartest. It's which platform removes the most friction for your practice.

In this episode:

• Why the AI world is moving from one-off prompting to persistent, repeatable workflow environments
• What connectors, APIs, MCPs, plugins, and skills actually mean — and how they differ from each other
• What Claude for Legal launched: 22 connectors, 12 plugins, and MCP architecture designed for enterprise legal environments
• Why Claude for Legal is largely out of reach for solo and small firm lawyers — and why that's not necessarily a problem
• What StrongSuit is, how its divorce and family law workflow presets work, and what "blank prompt box anxiety" means for practicing lawyers
• How Markdown (.md) workflow files work, why they're portable across frontier AI tools, and how Ron built his own

We also discuss:

• Why Consumer Claude Pro is not three-legged stool compliant for confidential client data
• The M&A demo Anthropic ran at their Claude for Legal launch — and what it signals about their actual target market
• How enterprise platforms like Harvey, Copilot, and Gemini fit into the ecosystem track
• Vertical specialty tools beyond StrongSuit: Litmus.ai, Glade.ai for bankruptcy
• The "blank page anxiety" finding from the big law associates episode and how presets address it

Key Takeaway

The AI tool that wins your practice isn't the one with the most connectors or the highest benchmark scores. It's the one that eliminates the friction between where you are and where the finished work product needs to be. Presets and persistent workflows do that in a way raw prompting never could.

If you're a Simpsons lawyer — aware of AI, dabbling, maybe running isolated prompts — this episode is your map. You don't have to build enterprise infrastructure. You need to identify one workflow bottleneck in your practice and find the tool that addresses it specifically. For family law lawyers, StrongSuit may already exist. For others, a Markdown workflow file built with your AI may be closer than you think.

Mentioned in This Episode

• Claude for Legal — Anthropic's official announcement (22+ connectors, 12 plugins, MCP architecture)
  StrongSuit — divorce and family law AI workflow platform
  Model Context Protocol (MCP) — official documentation
  Harvey
  iManage
  NetDocuments
  Thomson Reuters Co-Counsel
  Westlaw
  Free Law Project
  DocuSign
  Litmas.ai
  Glade.ai
  Gemini
  Microsoft Copilot
  Ivory Mind
  ChatGPT / OpenAI GPTs
  Google Gems

Prior podcast episodes:

Episode 007: Folder Mania — AI Comes to You
Field Note: I Wanna Hold Your Hand — Learning AI from AI
How BigLaw Associates Are Actually Using AI in Legal Drafting 

[email protected]

When AI becomes a privilege problem, most lawyers are still treating it like a productivity hack.

Solo and small firm attorneys hear constantly that AI saves time. What they hear far less often is that the AI tool they chose — and more specifically, the tier they're using — may have just waived their client's privilege. This episode forces that conversation. If you're putting client material into any AI tool without understanding exactly how that tool handles your data, you're not just taking a risk — you're potentially handing opposing counsel a gift.

In this episode:

• Why the tier of AI tool you're using (free, Pro, Enterprise) is a privilege and confidentiality issue, not just a performance issue
• The U.S. v. Heppner case: how using Claude at the Pro tier — not Enterprise — led to a court finding that confidential materials weren't protected
• The Trembly v. OpenAI case (2024 U.S. Dist. Lexis 141362) and what it establishes about AI outputs as opinion work product
• Why Harvey's architecture makes it suitable for confidential client material when Claude's public tiers do not
• How to build privilege defensibility into your AI workflow: mandatory human review, output labeling, and policy documentation
• Matt Lafferman's framework for ensuring AI outputs qualify as opinion work product rather than discoverable fact work product
• Ron's "record everything" hot take — and Matt's push back on where that logic breaks down in sensitive matters
• How in-house counsel faces a unique AI challenge because business and legal functions blur, and only the legal portions are privileged
• Automating law firm intake: what tools like Clio, MyCase, and PracticePanther are building, and what Dentons is already doing

We also discuss:

• How Dentons uses Harvey as a document vault, including running tiered relevancy scoring on large document sets
• The README file as the next frontier: why tech-sector in-house counsel may need to rethink document formats entirely
• Ken Griffin's reversal on AI — from calling it "garbage" in January to expressing alarm at what agentic AI is doing to PhD-level work
• Whether AI saves time or elevates work product quality — Ron and Matt respectfully disagree
• The challenge AI poses for junior associate development and entering the legal market right now
• The FSJ closing segment: Flintstones, Simpsons, and Jetsons advice from a Big Law AI task force member

• When and how to disclose AI use to opposing counsel
• Language for protective orders that includes AI tools in the definition of authorized agents
• How to frame AI outputs as generated at the direction of counsel
• Adaptable to different jurisdictions and risk profiles

Key Takeaway

Availability is not authority — and that principle extends to tool tiers. Using an AI tool that collects your prompts, trains on your outputs, and discloses data to third parties isn't just a privacy concern. It's a privilege waiver waiting to happen. Matt Lafferman's framework is straightforward: choose the right tool, mandate human review, mark everything as work product, and document your policy so you can show a court exactly how your AI workflow maintains privilege at every step.

For Flintstones lawyers, this episode is a fire alarm — the risks are real and courts are already ruling on them. For Simpsons lawyers using Claude Pro or a free tier for anything client-adjacent, this is the moment to audit your setup. Jetsons lawyers building custom agents should be baking these privilege protections into their workflow architecture from day one, not retrofitting them after a discovery dispute.

Mentioned in This Episode

• Matt Lafferman, Partner, Dentons (white collar, government investigations, crypto/blockchain, AI task force)
• Harvey (enterprise AI platform for legal)
• Claude (Anthropic) — Pro tier vs. Enterprise tier distinction
• Microsoft Copilot
• Clio
• MyCase
• PracticePanther
• U.S. v. Heppner — Claude Pro tier, privilege, and work product
• Trembly v. OpenAI, 2024 U.S. Dist. Lexis 141362 — AI outputs as opinion work product
• Ken Griffin / Citadel — remarks at Stanford Business School on agentic AI
• Judge Rakoff, Southern District of New York
• Rule 26(f) AI Discovery Protocol Addendum
• Notice of Intent to Use AI in Discovery
• README / .md files as emerging document format for in-house counsel
• WhatsApp communications in crypto litigation
• Daubert motions
• Ron's "record everything" CLE hot take

The AI training market for lawyers is broken. Not because there isn't enough of it — there's more than ever. The problem is almost all of it is aimed at the wrong lawyer. LinkedIn is full of Jetsons lawyers talking to other Jetsons lawyers, while the majority of practitioners are still trying to figure out how to create a PDF. So if the training doesn't meet you where you are, what do you actually do?

In this episode:

• Why the explosion of AI training has created more confusion, not less, for solo and small firm lawyers
• The "screenshot, upload, prompt, repeat" method Ron uses to navigate new software with AI as his guide
• Why AI has become an always-on tutor — and where that tutor reliably falls short
• The maze-solving metaphor: how AI-guided learning gets you there eventually, but not always efficiently
• Three diagnostic questions to ask any AI trainer before you spend a dollar or an hour
• Why governance is no longer a procedural afterthought — it's substantive, and good training has to address it
• Two new downloadable deliverables: a trainer vetting checklist and a due diligence inquiry template

We also discuss:

• Why Microsoft and Google already have excellent AI training most lawyers don't know they have access to — Microsoft Copilot legal training (Microsoft Learn) · Google AI Skills Hub · Google Workspace AI for Legal
• The real value of human trainers: shortcuts, judgment, accountability — things AI can't reliably replicate
• A real example of a law firm that made AI training stick through scheduled, team-wide calendar commitment
• Ron's earlier Field Note on taking screenshots — and why, in retrospect, that wasn't absurdly basic at all — Episode 003: Specialist AI Tools
• The hallucination field note: 21 ways AI Hallucinates in your Legal Brief — download at lawyeraitoolkit.com/deliverables

• Three diagnostic questions to assess whether a training program fits your FSJ level
• Whether training is workflow-focused or just a feature parade
• How to assess whether the program takes hallucinations, confidentiality, and governance seriously
• A ready-to-send email/DM template for due diligence outreach to prospective trainers

Key Takeaway

AI is an infinitely patient tutor. It will walk you through the maze, one wall at a time, and it will eventually get you there. But it won't always get you there efficiently, and it won't always get you there correctly — especially when its training data is three years out of date. The real skill is knowing when to use the bot and when to find the human who can point you at the exit in thirty seconds.

This episode speaks directly to Simpsons lawyers who are doing what Simpsons lawyers do: picking up AI tools, bumping into walls, and figuring it out one screenshot at a time. But Flintstones lawyers who haven't entered a single prompt yet will find the framework here — especially the three questions — genuinely useful before they spend anything. And Jetsons lawyers building agent workflows have likely already internalized everything Ron says. This one isn't for them.

Mentioned in This Episode

• Claude (Anthropic)
• ChatGPT (OpenAI)
• Google Gemini
• Microsoft Copilot
• GoHighLevel
• Cogent Marketing
• Google Drive · Microsoft Word · Zoom
• Heather Gardner (co-host)
• FSJ Framework (Flintstones / Simpsons / Jetsons)
• Ron's Field Note: Confessions of an AI Hallucinator
• 21 Ways AI Can Hallucinate in Your Legal Brief — download
• lawyeraitoolkit.com/deliverables

I recently participated in a live AI panel at the Maryland Bankruptcy Bar Association Spring Break Weekend — one of the major annual CLE and networking events for Maryland bankruptcy lawyers.

The panel featured retired federal judge Paul Grimm as moderator, along with Patti Jefferson, Nancy Rapoport, and Ron Drescher. But this episode is not simply a replay or recap of the panel.

Instead, we use the experience to explore a much bigger question:

Is the legal profession actually teaching AI effectively?

In this episode:

• Why AI CLE panels may struggle to teach lawyers at vastly different technology levels
• The continuing evolution of the Flintstones / Simpsons / Jetsons framework
• Why some lawyers may never need to become “Jetsons-level” AI users
• Patti Jefferson’s live AI demonstration using the Red Lobster bankruptcy confirmation order
• The rise of agents and workflow automation beyond traditional prompting
• Nancy Rapoport’s warnings about hallucinations, supervision, and professional responsibility
• Why “hallucination verification” may erase much of AI’s promised time savings
• A comparison between AI hallucinations in law versus medicine
• Why many firms remain stuck at “sub-Flintstones” technology levels
• How to identify the pain points inside your law firm before adopting AI
• Why your firm should conduct a “Tech Stack Audit”

We also discuss:

• Dropbox and document organization for high-volume bankruptcy practices
• The growing divide between consumer AI and enterprise AI
• Why “ChatGPT” is no longer a meaningful description without understanding the underlying plan and governance structure
• The importance of the “Three-Legged Stool” for safe legal AI deployment:
  • Vendor protections
  • Proper configuration
  • Human supervision

Download: Tech Stack Audit Spreadsheet

This episode includes a downloadable spreadsheet template designed to help lawyers:

• identify all software subscriptions,
• calculate monthly and annual costs,
• evaluate what they are actually getting from their tech stack,
• and determine where they fall on the Flintstones / Simpsons / Jetsons spectrum.

If you complete the spreadsheet and would like us to discuss it anonymously (or publicly) on a future episode, send it to:

[email protected]

We’d love to see how lawyers are actually building — or struggling to build — their AI and technology infrastructure.

Mentioned in This Episode

• ChatGPT
• Claude
• Gemini
• Harvey
• IvoryMind
• Google Workspace
• Dropbox
• Clio
• Foundation AI

Key Takeaway

Most legal AI education still treats lawyers as if they are all at the same level of technological fluency.

But Flintstones lawyers, Simpsons lawyers, and Jetsons lawyers may not even be attending the same CLE — even if they are sitting in the same ballroom.

This Field Note is a direct companion to the episode “21 Ways AI Can Hallucinate in Your Legal Brief.”

If that episode showed how AI fails, this one shows how lawyers are adapting anyway.

Drawing from a real-world Reddit thread with dozens of BigLaw associates, this episode breaks down the actual workflows lawyers are using today—not theory, not vendor demos, and not CLE talking points.

What emerges isn’t a list of tips. It’s a set of patterns.

And those patterns reveal something important:

AI isn’t replacing legal drafting.
It’s reshaping how drafting gets done.

🔑 Key Takeaways

• AI is used for structure and volume—not judgment
• Lawyers are using AI to break the blank page problem
• Drafting works best when done in small, controlled sections
• Strong workflows emphasize outline → structure → prose
• Effective users rely on iteration, not one-shot prompting
• AI is highly effective for rewriting, organizing, and clarity
• Many lawyers now treat AI like a junior associate
• AI is increasingly used as a thinking partner, not just a drafting tool
• There is near-universal agreement:
  ⚠️ Do NOT trust AI for citations or legal authority

⚠️ The Core Insight

Across all 12 patterns, one principle stands out:

AI handles the work.
The lawyer handles the responsibility.

👤 For Solo & Small Firm Lawyers

BigLaw associates operate with built-in review layers.

If you don’t have that safety net, these same workflows require:

• Greater discipline
• More deliberate verification
• A clearer understanding of where AI fails

🔗 Companion Episode

🎙️ Field Note: 21 Ways AI Can Hallucinate in Your Legal Brief

Use both together:

• One shows you how AI breaks
• This one shows you how lawyers are adapting

📥 Downloadable Companion Resource

A structured breakdown of all 12 drafting patterns is available on the Deliverables page:

👉 https://lawyeraitoolkit.com/deliverables

Use it as a practical reference when building your own AI drafting workflow.

🎯 Final Thought

The question isn’t whether lawyers should use AI in drafting.

They already are.

The real question is:

Do you know exactly where AI stops being reliable?

In this Field Note, Ron Drescher breaks down one of the most important—and misunderstood—risks in legal AI: hallucinations.

The episode begins with the recent Sullivan & Cromwell filing admitting AI-generated errors, with a close look at the now-famous Schedule A. While most commentary has focused on fake citations and misquotes, Ron highlights the more subtle—and more dangerous—types of hallucinations that appeared in that filing.

From structurally corrupted citations to mutated judicial language, this episode explores how AI doesn’t just make obvious mistakes—it makes mistakes that look like law.

Ron then expands the discussion to a broader framework, identifying both the most well-known hallucination risks and the lesser-known categories that are more likely to survive verification and make their way into filed briefs.

⚖️ What You’ll Learn

• Why the Sullivan & Cromwell Schedule A is more important than the confession letter
• Two underappreciated hallucinations:
  • Citation drift (hybrid citation corruption)
  • Mutated quotations
• The 3 most common AI hallucinations:
  • Fabricated cases
  • Real cases with incorrect holdings
  • Invented quotations
• Three lesser-known (and more dangerous) hallucinations:
  • Subtle semantic drift
  • Fake multi-case consensus
  • Logical hallucination (broken arguments that look complete)
• Why “just verify the citation” is no longer enough
• A practical verification framework for AI-assisted legal writing

🧠 Key Takeaway

AI hallucinations are no longer edge cases—they are part of the operating environment of modern legal writing.

The real risk isn’t obvious errors.

It’s the errors that:

• look correct
• pass a quick check
• and still make it into your brief

📥 Downloadable Resource

This episode includes a companion Field Note:

👉 “21 Ways AI Can Hallucinate in Your Legal Brief”

Use it as a working reference during your hallucination verification process—not as a one-time read.

🔧 The New Verification Standard

Before including any authority in a brief, confirm:

• Does the case support the proposition?
• Is the quote accurate and in context?
• Does the procedural posture match your argument?
• Has the legal standard shifted subtly?

🔜 Coming Next

Field Note:
12 Ways BigLaw Associates Are Quietly Optimizing AI in Legal Drafting

A practical look at how lawyers in high-stakes environments are adapting their workflows to use AI effectively—without getting buried in verification.

🎙️ About the Show

AI Tools for Practicing Lawyers delivers practical, no-nonsense guidance to help attorneys put AI to work in their practice right now.

Hosted by Ron Drescher, a retired bankruptcy attorney with over 40 years of experience, the show focuses on real workflows—not hype.

There comes a moment for every lawyer using AI when experimentation turns into real-world application. That’s where the real opportunities begin.

Ron and Heather talk to Colorado bankruptcy attorney Matt McCune, a 25-year practitioner who isn’t just talking about AI—he’s rebuilding his law practice around it.

Matt shares how AI is transforming the entire structure of a law firm and why the lawyers who embrace it thoughtfully will define the next generation of legal service.

Along the way, the conversation explores the tension between scale and responsibility, the importance of human oversight, and how AI can elevate—not replace—legal judgment.

Key Takeaways

1. AI as a Force Multiplier (Not Just a Time Saver)

AI isn’t just about speed—it’s about removing friction from everything surrounding the practice of law.

• Automating intake, communication, and SOPs
• Reducing repetitive client interactions
• Freeing lawyers to focus on judgment and strategy

2. Human-in-the-Loop Is Non-Negotiable

AI works—but only with oversight.

• Systems can glitch
• Workflows need validation and fallback checks
• Lawyers remain responsible for outcomes

3. AI Improves Client Experience (Where Lawyers Struggle Most)

The biggest complaint in legal services? Communication.

AI enables:

• Instant responses
• Automated updates (e.g., objections, case status)
• Pre-recorded or AI-generated client prep (e.g., 341 meetings)

4. Real-World Workflow Example: Client Prep Automation

Matt uses tools like HeyGen to:

• Deliver automated video explanations to clients
• Prepare them for key moments (like 341 hearings)
• Replace repetitive phone calls

Result: better-prepared clients and less time spent repeating the same explanations.

The FSJ Framework: How Lawyers Actually Adopt AI

This episode naturally walks through the full Flintstones → Simpsons → Jetsons progression:

🪨 Flintstones Lawyer

• Start simple: download an AI app
• Use voice mode while driving
• Treat it like a conversation with a colleague

🍩 Simpsons Lawyer

• Move from dabbling to building small assets
• Example: create a simple landing page using AI tools
• Begin experimenting with workflows

🚀 Jetsons Lawyer

• Build integrated systems
• Use closed AI environments for client data
• Automate document analysis and workflows

Practice Signal: Client Communication Breakdown

A real-world scenario highlights a common issue:

• Anxious clients
• Delayed responses
• Escalation to third parties

AI Solutions:

• Instant acknowledgment emails
• Automated status updates
• Tone-optimized responses
• Ethical analysis before responding

This is where AI shines—not in legal brilliance, but in consistent, empathetic communication at scale.

What’s Next

Matt McCune will be presenting at an upcoming National Association of Consumer Bankruptcy Attorneys webinar “Stop Dabbling, Start Deploying” a two-part series focused on real-world implementation

Resources Mentioned

• Matt McCune Substack BankruptcyAttorney.ai
• AI video tools -HeyGen
• Crossing the Chasm

Most legal AI conversations focus on drafting, research, discovery, and analysis. But what if some of the most valuable AI for law firms has nothing to do with writing?

In this Workflow Options minisode, Ron Drescher explores Foundation AI, a company focused on one of the oldest and most frustrating business problems in history: incoming document chaos.

Ron revisits his Four Buckets of a Law Firm framework:

1. Sales / Marketing / Revenue
2. Production / Fulfillment
3. Operations / Administration / HR
4. Personal & Professional Development

He explains why so much legal AI attention is aimed at Bucket #2 (drafting and legal work), while Bucket #3—operations—may offer some of the safest and fastest ROI opportunities.

Foundation AI appears to operate in Ron’s green-light AI zone by helping firms:

• ingest incoming documents
• identify what they are
• match them to the correct matter
• rename files properly
• place them in correct folders
• trigger tasks and alerts
• reduce delays and manual handling

Ron also discusses Casepeer, the PI-focused case management platform featured in the webinar, where Foundation AI was presented as the workflow engine feeding the operational hub.

A key caveat: Ron notes that Foundation’s current workflow appears tied to Outlook, with Gmail integration expected in the future—a major development if true, given how many firms operate inside Google ecosystems.

This episode also previews future discussions on Microsoft vs Google ecosystems for law firms, and why that choice may become increasingly strategic.

Sometimes the most valuable AI in law doesn’t write a brief.

It just gets the right PDF into the right file at the right time. 

What’s the cheapest way for a lawyer to start using AI without creating expensive ethical, security, or sanctions problems?

In Episode 010, Ron Drescher and Heather Gardner tackle one of the most common questions lawyers are asking right now: I’m ready to try AI, but I don’t want to pay an arm and a leg—and I don’t want to get into trouble.

The conversation starts with the premium end of the market, including Harvey and other enterprise legal AI products built for large-firm workflows, governance, and document intelligence. But most solos and small firms need practical, affordable solutions—not BigLaw pricing.

Ron and Heather explain the “AI tech stack” concept: choosing tools based on what work you actually need done rather than chasing hype. They revisit the Jeffers three-legged stool framework for legal AI governance: vendor security, proper configuration, and responsible lawyer oversight.

They then break down the current budget-friendly options, including Google, OpenAI, Anthropic, and Microsoft—with a strong case that Gemini for Workspace may be the best low-cost compliant starting point for many firms.

The episode also features a Practice Signal from Reddit’s BigLaw world: a burned-out associate billing 2400 hours and wondering whether there’s a better path. Can AI help lawyers build more autonomous practices outside traditional BigLaw structures?

Finally, the Flintstones–Simpsons–Jetsons segment recommends books for every stage of AI fluency:

Flintstones:
- A Lawyer's Guide to AI by Matthew T. Henshon (ABA, 2026)
  https://www.amazon.com/Lawyers-Guide-AI-Essential-Concepts/dp/163905684X
- AI for Lawyers by Noah Waisberg & Alexander Hudek
  https://www.amazon.com/Lawyers-Artificial-Intelligence-Transforming-Profession/dp/1119723841

Simpsons:
- Co-Intelligence by Ethan Mollick
  https://www.amazon.com/Co-Intelligence-Living-Working-Ethan-Mollick/dp/059371671X
- Tomorrow's Lawyers (3rd Ed.) by Richard Susskind
  https://www.amazon.com/Tomorrows-Lawyers-Introduction-your-Future/dp/0192864726

Jetsons:
- The Coming Wave by Mustafa Suleyman
  https://www.amazon.com/Coming-Wave-Technology-Twenty-first-Centurys/dp/0593593952
- The Future of the Professions (Updated) by Richard & Daniel Susskind
  https://www.amazon.com/Future-Professions-Technology-Transform-Experts/dp/0198841892

Key Topics Covered

• Why “cheap AI” can become very expensive if it creates risk
• What premium tools like Harvey offer—and why they cost more
• The Jeffers framework for safe AI adoption in law firms
• Why lawyers should choose AI based on workflow pain points
• Gemini for Workspace as a budget-conscious legal AI option
• ChatGPT Enterprise vs Claude vs Copilot comparisons
• BigLaw burnout and using AI to build independence
• Building an effective lawyer AI stack over time

Mentioned in This Episode

• Harvey
• Google
• OpenAI
• Anthropic
• Microsoft

About the Show

AI Tools for Practicing Lawyers delivers practical strategies for the modern lawyer—helping attorneys use AI safely, effectively, and profitably in real-world practice.

Even elite firms can get burned by AI hallucinations. In this field note, Ron Drescher breaks down the recent Sullivan & Cromwell filing controversy, where an emergency brief reportedly contained multiple citation and quotation errors that opposing counsel exposed first. The lesson is not that one firm slipped—it’s that polished AI output can create false confidence in any lawyer, especially when they're under severe deadline pressure.

Ron connects the story to his earlier Confession of an AI Hallucinator episode (where he confessed to sending out a memo containing hallucinated cases) and explains why time-stressed emergency filings are fertile ground for hallucination mistakes. He then pivots to a practical alternative: using AI as a research guide, not a research substitute.

Instead of relying on AI to generate authorities directly, Ron proposes using AI to create multiple Boolean search strategies, help navigate Westlaw/Lexis/Bloomberg features, and improve the research process while keeping lawyers anchored to real databases, real cases, and real citations.

The episode also introduces Ron’s “airport metaphor” for after-the-fact AI verification: if the promised shortcut requires hours of extra checking after the brief is drafted, maybe the traditional route would have been faster all along.

Key Topics Covered

• Why AI hallucinations are not just a solo/small-firm problem
• What happened in the Sullivan & Cromwell filing controversy
• Why emergency motions and deadline pressure increase hallucination risk
• The danger of polished but false AI output
• Why Ron is skeptical of AI-as-research-substitute workflows
• Using AI to generate smarter Boolean searches
• Using AI to help master legal research tools you already pay for
• Building briefs from verified authority rather than unverifiable AI citations
• The airport metaphor for inefficient AI verification workflows

Featured Insight

“This is what using the after the fact AI verification technique is like; if you have to go through this whole verification process after writing your brief, maybe you would have been better off using the old fashioned research tools instead.”

Resources & Deliverables

• Sullivan & Cromwell apology letter with Schedule A of disclosed citation errors: https://lawyeraitoolkit.com/deliverables

About the Show
AI Tools for Practicing Lawyers delivers practical, no-nonsense guidance to help attorneys use AI safely, effectively, and profitably in the modern practice of law.