AI Tools for Practicing Lawyers

Are "build an AI agent in 20 minutes" ads lying to you?  Ron spent way more than 12 hours trying to find out.

Ron set out to build the simplest possible AI legal workflow — a motion to extend time to file bankruptcy schedules — and discovered that "easy" and "AI-assisted" don't always mean the same thing.

In this episode:

• Why Ron decided to test AI workflow-building himself instead of buying a course
• The 10-step workflow development cycle ChatGPT walked him through, from defining objectives to platform optimization
• Testing the same workflow across Claude, ChatGPT, Gemini, and Copilot
• Why Google Docs + Gemini unexpectedly produced the best output of any platform tested
• Why Copilot's deep integration with Word actually made it harder to use, not easier
• ChatGPT's pushback on regenerating an entire document — and the hospital-acquired-infection analogy Ron uses to explain AI "drift"
• The lesson that mature AI users isolate and fix specific problems instead of regenerating whole documents
• Why Ron avoided using Claude for this project due to a prior usage-limit experience on the Pro tier
• The "airport test" from Ron's prior Field Note, Confessions of an AI Hallucinator, and how it applies to building workflows
• The free "Motion to Extend Lite" workflow Ron is releasing publicly

We also discuss:

• How persistent AI workflows (skills, agents, StrongSuit-style systems) represent a brand-new product category
• The decision points Ron had to map for an "omnibus" extension workflow covering any deadline type
• Quality control checks built into the workflow — missing dates, missing deadlines, missing cause language
• How Ron accidentally discovered this entire approach through his own podcast post-production process
• Why bare-bones bankruptcy petitions create the exact problem this workflow solves
• The risk of inconsistent details (names, dates, captions) when reusing forms across cases — and how AI reduces that risk

Key Takeaway

Building a workflow that actually works — across platforms, with minimal user friction, passing real-world testing — is a fundamentally different job than writing a clever prompt. The platform you choose matters as much as what you ask it to do, and the AI tool that's "supposed" to be best for the job (Copilot in Word, Claude for heavy drafting) isn't always the one that delivers.

For Flintstones lawyers, this episode is proof that a usable AI workflow can exist without them building anything — Ron's free download does the work. Simpsons lawyers will recognize the platform-testing process as the real work of AI adoption. And Jetsons lawyers will appreciate the granular lesson on regeneration risk and isolating fixes rather than reprocessing entire documents.

Mentioned in This Episode

• ChatGPT
• Claude (Pro tier)
• Google Gemini (Google Docs integration, Enterprise tier)
• Microsoft Copilot
• StrongSuit
• Confessions of an AI Hallucinator (prior Field Note episode — airport test)
• Team Accelerator (Ron's bankruptcy training course)
• Motion to Extend Lite (free workflow download)

SHOW NOTES

NOTE: The File Cabinet Test is used to determine whether AI can discern the contents of every file in a connected drive.

The star of Apple's WWDC 2026? Apple Intelligence. Apple announced how Apple Intelligence is being incorporated into Siri. This prompts us to ask: Can Apple Intelligence finally make workflow automation accessible to ordinary lawyers?

In this episode:

• Why Apple's AI strategy isn't about building the smartest model — it's about removing friction
• How natural language Shortcuts could lower the barrier to automation for lawyers who've never written a line of code
• Whether iCloud can finally compete with Google Drive and OneDrive as an AI-connected file system
• Apple's private cloud compute architecture and why privacy is not the same as privilege
• Why the real story from WWDC 2026 may be the democratization of workflow automation

We also discuss:

• Ron's history as an Apple fanboy since the 1980s — and why this keynote actually got his attention
• Steve Jobs' "best camera" line and what it means for AI adoption
• Ron's existing Claude-based podcast workflow as a real-world example of what natural language Shortcuts could replicate
• The fact that none of these features have shipped yet — these are observations, not a review
• Siri's rebranding struggle and Apple's decision to stick with the name despite years of ridicule

Key Takeaway

The biggest AI story from WWDC 2026 isn't that Apple built the smartest AI. It's that Apple appears to be trying to make AI invisible — woven directly into the operating system rather than siloed in a separate app or chatbot (although there will be a stand-alone chat style Siri app). If natural language Shortcuts works the way it was demonstrated, lawyers may be able to build automation workflows simply by describing what they want in plain English.

That's not a Jetsons story. That's a Simpsons story — and maybe even a Flintstones story. Prompting is useful, but workflows are transformational. If Apple pulls this off, the gap between lawyers who automate and lawyers who don't may finally start to close.

Mentioned in This Episode:

• Apple Intelligence
• WWDC 2026
• Shortcuts (Apple)
• Siri
• iCloud
• Finder
• Gemini (Google)
• Claude
• ChatGPT
• Google Drive
• OneDrive / Microsoft
• Apple Private Cloud Compute
• The File Cabinet Test (from Episode 007 FolderMania)

[email protected]

Most conversations about AI and legal writing focus on the tools. This one focuses on the lawyers. What does a Flintstones lawyer actually do on Monday morning after they've finally decided to move? What does a Simpsons lawyer do when they discover their favorite tool isn't safe for client data? And what happens when the question isn't whether to adopt AI — but whether you'll survive professionally if you don't? Ron sits down with co-host Heather Gardner and Maryland attorney and legal educator Donna Mandl to work through the questions practicing lawyers are actually asking — the ones that never make it into the marketing decks.

In this episode:

• Why a Flintstones lawyer's first move should be calling their Westlaw or Lexis rep — not downloading a new app
• Why Heather recommends Enterprise ChatGPT as the right entry point for lawyers handling client information
• The "Claude as cleanup hitter" workflow: how to keep confidential work in a secure tool and bring the output to Claude for drafting
• Gemini's hidden advantage — and why most lawyers are using it wrong
• The hallucination problem everyone's talking about — and why fake citations aren't the real crisis
• The subtler risk: cases that exist but don't say what AI claims they say, and standards of review that get quietly swapped
• Donna's paralegal education pivot — from policing AI use to training students to audit what AI produces
• Ron's prediction: by end of this decade, there will be no more Flintstones-level lawyers

We also discuss:

• What Heather and Donna are presenting at the Maryland State Bar Association's Legal Summit panel — Ethics, Accuracy, and Efficiency: AI in Legal Writing
• Why judges are getting frustrated with both pro se AI filings and inaccurate AI-assisted briefs from lawyers
• Ron's Claude experiment: feeding Claude its own 21 hallucination types and asking how many the new model would fix (14 of 21 — 7 remain hard)
• The FSJ-client alignment theory: Flintstones clients are disappearing, and Flintstones lawyers will have to follow
• FSJ-segmented follow recommendations: Dan Block (Flintstones), Ruben Hassid (Simpsons), Rich Rodgers (Jetsons)
• The Practice Signal segment: can AI help a burned-out workers' comp lawyer find a new career?

Key Takeaway

The governance question for legal AI isn't philosophical anymore — it's a billing-line decision. Whether it's a $1,400-a-year Claude Enterprise commitment or a workflow choice about which tool sees client data, the lawyers who figure out the tiers will outpace the ones still treating a free-tier tool as a research platform. Availability is not authority — and neither is a consumer account.

Flintstones lawyers who hear this episode have a clear Monday-morning move. Simpsons lawyers who've fallen for Claude but balked at the enterprise price now have a workaround. And Jetsons lawyers will recognize the gap is widening faster than most of their colleagues realize. Heather said it best: prompting got us from Flintstones to Simpsons. Learning to think and collaborate with AI is what takes you to Jetsons.

Mentioned in This Episode

• Heather Gardner — co-host, AI Tools for Practicing Lawyers
• Donna Mandl — Maryland attorney; legal educator, Community College of Baltimore County (LinkedIn)
• Shaun Koenig — Maryland attorney, MSBA Legal Summit panelist (LinkedIn)
• Maryland State Bar Association Legal Summit — Ethics, Accuracy, and Efficiency: AI in Legal Writing panel
• ChatGPT (OpenAI) — free tier and Enterprise tier
• Claude (Anthropic) — Pro tier and Enterprise tier
• Claude Code (Anthropic)
• Harvey
• Google Gemini — consumer and Enterprise ($36/month) tiers
• Westlaw (Thomson Reuters)
• LexisNexis
• Fastcase
• Rich Rodgers — prior guest, Episode 014; founder, StartupTechLaw (LinkedIn)
• Dave Block — legal AI commentator; recommended follow for Flintstones lawyers (LinkedIn)
• Ruben Hassid — Claude evangelist; recommended follow for Simpsons lawyers (LinkedIn)
• Field Note: 21 Ways AI Can Hallucinate in Your Legal Brief
• Field Note: Tiers of the Clown 
• Mezu v. Mezu, Maryland Appellate Court No. 361 (2025)

The question everyone is asking is wrong.

When lawyers debate which AI tier is "smarter," they're arguing about a label — and labels end analysis. The better question isn't which tier is best. It's what capabilities am I actually buying, and whether those capabilities match the task in front of you. Ron's File Cabinet Test experiments proved this the hard way: Enterprise-tier AI passed tests his Plus account failed. But when it came time to brainstorm a podcast episode, he went right back to Plus. Not because it was smarter. Because it knew him.

In this episode:

• Why "smarter" is a label that ends analysis instead of starting it
• The six capability categories that actually differentiate AI tiers: context window, retrieval, usage limits, connectors, memory, and governance
• How Ron's File Cabinet Test revealed a material performance gap between Plus and Enterprise environments
• Why the best AI tier is the one whose capabilities and accumulated context match the task — not the one with the highest price point
• Why governance and capability are different things, and why you need both
• The framework for evaluating AI tiers that survives even when pricing, features, and model names change

We also discuss:

• How a Reddit thread about AI tiers triggered Ron's thinking on this episode
• Why Ron returns to his Plus account for podcast brainstorming even after seeing Enterprise outperform it
• The companion handout for this episode (and why it may already be partially obsolete)
• How ChatGPT, Claude, and Gemini package the same core capabilities in different ways
• The analogy of the context window as the size of an AI's desk

Key Takeaway

Stop asking which AI is smartest. Start asking which capabilities matter for the task at hand. Retrieval isn't reasoning. Governance isn't performance. Context is accumulated over time, and a tool that knows your practice may outperform a more capable tool that doesn't. The best tier is the one aligned with what you're actually trying to do.

For Flintstones lawyers, this episode removes the paralysis. You don't have to figure out which AI won. For Simpsons lawyers who've already paid for something, this is the framework for deciding whether they bought the right tier — or just the most expensive one. Jetsons lawyers will recognize the capability taxonomy immediately and probably already live by it.

Mentioned in This Episode

• ChatGPT (OpenAI)
• Claude (Anthropic)
• Gemini (Google)
• Heather Gardner (co-host, Enterprise environment)
• File Cabinet Test (Ron's benchmark framework)
• Folder Mania experiments
• Three-Legged Stool (compliance framework — see Field Note: Building the Stool — How to Implement the AI Discovery Standards)
• Flintstones/Simpsons/Jetsons Framework
• Companion handout (available at lawyeraitoolkit.com/deliverables)
• Reddit

SHOW NOTES 

Is the billable hour a liability you're voluntarily handing to your clients — and is AI finally giving lawyers a way out?

Rich Rodgers has been building AI-native legal tools since before most lawyers knew what a large language model was. He's a practicing startup attorney, a four-time founder, and the creator of Start Legal — an AI platform designed to give founders a running start on legal work before they ever engage counsel. This episode isn't about whether AI will replace lawyers. It's about whether lawyers who refuse to adapt will replace themselves.

In this episode:

• How Rich Rodgers built Start Legal out of 20+ custom GPTs originally created for his own clients at Startup Tech Law
• Why Rich charges $10,000 to review AI-generated client contracts — and why no one has ever taken him up on it
• The "Attorney Assist" model: how Start Legal pairs AI-generated documents with on-demand attorney review starting at $150 for 30 minutes
• What "AI-native law firm" actually means — and why it's harder to define than the people throwing the phrase around on LinkedIn let on
• The argument for scrapping the billable hour: if AI cuts a 5-hour task to 5 minutes, does the client owe you for the hours you didn't spend?
• Rich's AI-generated invoicing workflow — how he wakes up on the 1st of every month with all client invoices already built and ready to send
• The Delaware ruling allowing corporations and other artificial entities to vote in certain municipal elections — and what it might mean for corporate governance documents
• A concrete FSJ roadmap: what Flintstones-, Simpsons-, and Jetsons-level lawyers should actually do next

We also discuss:

• The em-dash problem: how a punctuation mark became a tell for AI-generated text (and Ron's personal defense of it)
• Whether AI-native practice is more natural in transactional work than litigation — and where the limits actually are
• The UK firm licensed to practice law as an AI — and the startup firm handling traffic tickets with no human attorney
• Whether Flintstones lawyers should only try to serve Flintstones clients, or whether tech alignment between lawyer and client matters
• Why Rich thinks the next generation of clients will arrive already fluent in Claude and GPT — and what that means for practices that aren't ready

Key Takeaway

Availability is not authority — and it's not a business model either. Clients are already arriving with AI-drafted contracts, AI-researched questions, and AI-generated documents they believe are finished products. The lawyers who treat that as a threat are the ones charging $10,000 for a GPT contract review as a way of saying no. The lawyers who treat it as an opportunity are building the tools, setting the terms, and staying in the loop on their own conditions.

The Flintstones lawyer's first move isn't to become a Jetsons lawyer overnight. It's to take whatever templates, clauses, and hard-won knowledge are sitting in a file cabinet — or a Microsoft Word folder — and start turning them into something that works for clients instead of just for the file. The Simpsons lawyer who's already prompting should be connecting those prompts to the operational infrastructure: billing, CRM, invoicing. The Jetsons lawyer is already doing what Rich is doing. The question for everyone else is how long the gap keeps widening.

Mentioned in This Episode

• Rich Rodgers — https://www.linkedin.com/in/richrodgers360/
• Start Legal — https://startlegal.com/
• Startup Tech Law — https://www.startuptechlaw.com/
• Claude (Anthropic) — https://claude.ai/
• ChatGPT / OpenAI GPT Store — https://chatgpt.com/gpts

[email protected]

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?