This module covers the AI tools available for legal work. We cover what tools exist, the concepts and technology behind them, and how to use them.
The Landscape
Legal Tech AI
Specialized tools built for legal work. Connected to legal databases with verified sources. Enterprise-grade security for confidential matters.
General AI
Versatile assistants for broad tasks. Drafting, brainstorming, summarization. Require more verification for legal accuracy.
Most lawyers will use both. The question is which tool to use for which task.
Here is a non-exhaustive list of legal AI tools that lawyers are familiar with. If you understand how these work, you can get a good idea as to what’s most effective for your practice.
Legal Tech
Legal-tech capabilities
Research-first platforms and legal workflow systems.
| Grounded Research | Authority Links | Document Analysis | Drafting Help | Workflow Actions | |
|---|---|---|---|---|---|
WWestlaw | — | ||||
⚙CoCounsel | |||||
✦Lexis+ AI | |||||
HHarvey | |||||
✓Clio | — |
Legal-tech feature diff
Expanded side-by-side view of where the legal platforms actually differ.
| Feature Diff | WWestlaw | ⚙CoCounsel | ✦Lexis+ AI | HHarvey | ✓Clio |
|---|---|---|---|---|---|
Authority-grounded research How directly the tool works from a proprietary legal corpus rather than generic web or model memory. | Native AI-assisted research runs inside Westlaw authority. | Native Research uses Thomson Reuters legal content, including Westlaw and Practical Law. | Native Conversational legal research sits directly on Lexis content. | Assisted Strong knowledge workflows, but research depth depends on firm setup and connected sources. | Limited Not a primary legal-research destination; AI is oriented around matter operations. |
Uploaded-document review Whether the platform is built to take in your files and analyze, summarize, or extract from them. | Limited Westlaw is strongest once you are inside its authority set, not as a general document-review surface. | Native Built for document review, summarization, extraction, and legal analysis. | Native Document Analysis is a first-class Lexis+ AI workflow. | Native Document and contract analysis are core Harvey product surfaces. | Assisted Matter documents drive summaries and next-step suggestions inside Clio workflows. |
Drafting and rewrite help How naturally the tool moves from research or matter context into producing editable work product. | Assisted Research outputs can seed drafting, but drafting is not the main surface. | Native Drafting is part of the main CoCounsel legal workflow. | Native Lexis+ AI supports drafting, summarization, and uploaded-document workups. | Native Ask, analyze, and draft are all core Harvey motions. | Native Clio drafts client updates, motions, letters, and matter communications in context. |
Matter and workflow automation Whether the platform can turn outputs into next actions inside an operational legal system. | Limited Westlaw is a research stack, not an execution system for firm operations. | Assisted CoCounsel connects work across Thomson Reuters tools, but it is still assistant-first. | Assisted Lexis+ AI helps with research and drafting more than end-to-end workflow automation. | Assisted Harvey helps generate work product, but workflow automation depends on deployment pattern. | Native Clio's AI is explicitly built to create tasks, deadlines, invoices, updates, and matter actions. |
Source trace and citator posture How directly the tool exposes source links, citator workflows, or validated legal provenance. | Native Authority links and citator workflows are part of the core Westlaw posture. | Native Outputs are framed around validated Thomson Reuters inputs and linked authority. | Native Lexis+ AI pairs answers with Lexis sources and Shepard's workflows. | Assisted Useful outputs still need source checking against the underlying authorities you trust. | Limited Clio is not the place you go for citation provenance or citator depth. |
General AI
General-AI capabilities
General assistants and answer engines used alongside legal-specific systems.
| Web Research | File Analysis | Drafting Help | Source Links | Reusable Workspace | |
|---|---|---|---|---|---|
◯ChatGPT | |||||
◈Claude | |||||
✧Gemini | |||||
⟡Perplexity |
General-AI feature diff
Expanded side-by-side view of where the general assistants differ in day-to-day legal use.
| Feature Diff | ◯ChatGPT | ◈Claude | ✧Gemini | ⟡Perplexity |
|---|---|---|---|---|
Live web and current-info research How directly the tool is built to pull in current web information during the answer. | Native ChatGPT can use web search for current, source-backed answers. | Native Claude web search is designed for live-web grounding with citations. | Native Gemini responses can draw on web-linked sources and double-check flows. | Native Perplexity is built around web search and sourced answer synthesis. |
File-grounded analysis Whether the tool can take in your materials and work directly from them instead of only from chat context. | Native Files can be uploaded for analysis, summaries, and shared workspace work. | Native Claude supports uploaded documents in chats and project knowledge. | Native Gemini supports uploaded documents, spreadsheets, code, images, and more. | Native Perplexity supports attached files and follow-up questions within the same thread. |
Persistent workspace and reusable instructions How much the tool supports a standing matter setup rather than a one-off prompt. | Native Projects group chats, files, and shared instructions under one objective. | Native Claude Projects provide knowledge, instructions, and project-level retrieval. | Native Gems package repeatable instructions and can be shared or edited. | Native Spaces combine instructions, sources, and thread history in one workspace. |
Source visibility How clearly the tool shows where the answer came from when you are doing research work. | Assisted Strong when search is invoked, but not every response is source-first. | Native Claude web replies cite sources, and Claude also supports document citations. | Assisted Gemini can show sources and related links when available, but not every reply has them. | Native Perplexity positions cited answers and source links as the main interaction pattern. |
Long-form drafting surface How well the tool supports turning research into an editable drafting or artifact workflow. | Native Canvas gives ChatGPT an explicit co-writing and editing surface. | Native Artifacts and file creation make Claude strong for iterative deliverables. | Native Gemini Canvas supports creating and editing docs, apps, slides, and code. | Assisted Perplexity is strongest for research synthesis; drafting is useful but not its signature mode. |
Features and pricing change frequently. Check each platform’s current product page for the most up-to-date information.
The Concepts Behind the Tools
Every AI tool listed above—legal tech or general—is built on the same set of underlying technologies. Understanding these concepts helps you evaluate any tool, including those that do not yet exist.
The Four-Layer Architecture
Most legal AI tools operate on four layers. The differences between tools come from how each layer is configured.
The Four-Layer Architecture
Interface
The chat window, document upload, and controls you interact with.
Determines the user experience and what workflows the tool supports.
AI Engine
The large language model that generates responses.
Different tools use different models. Most use third-party models like GPT or Claude.
Knowledge Base
The content the tool can access—legal databases, firm documents, the web, or only training data.
One of the biggest differentiators between tools. A tool like Westlaw augments its output with verified legal sources. A tool like ChatGPT does not.
Security Layer
Encryption, access controls, retention policies, training opt-outs.
Determines whether you can use the tool for confidential work. Review the TAR Check from Module 2.
RAG: Retrieval-Augmented Generation
RAG is a technology that most legal AI tools use to access a vast library of legal sources. Instead of relying solely on what the model learned during training, a RAG-enabled tool retrieves relevant documents from a database and includes them in the model’s context before generating a response.
Retrieval-Augmented Generation. Every handoff can still fail: a missed document, an outdated result, or a misread passage.
Think of it as the difference between a closed-book exam and an open-book exam. A pure language model answers from memory, while a RAG-enabled tool can look things up before answering. The tool searches its knowledge base for documents relevant to your query, pulls them into the model’s working memory, and then generates a response grounded in those documents.
While legal tech companies have said that their RAG tools eliminate hallucinations, this does not seem to be the case. The retrieval system can miss relevant documents, return outdated ones, or surface results that the model misinterprets. The model can also ignore what it retrieved and generate from the training data anyway.
This is why hallucination rates for legal tech tools, while lower than general AI, are still significant. RAG improves the tools by giving the model verifiable sources that can help mitigate hallucinations, but it does not make them reliable enough to skip verification.
How a Westlaw-style RAG query works in practice
Retrieval narrows the source set first. The model answers second.
Natural-language research ask
Has any federal court held that using a consumer AI tool can waive attorney-client privilege? Prioritize federal authority and explain the reasoning.
Westlaw ranks the authority set
The system searches the legal database, narrows by jurisdiction and topic, and selects a small set of opinions, headnotes, and secondary sources.
The model reads grounded excerpts
Only the retrieved passages and metadata go into the model context window, so the answer is anchored to specific authorities instead of pure model memory.
"Disclosure to a consumer AI platform may destroy confidentiality when the user accepts data-retention terms."
"Work-product analysis may differ if the tool is treated as an internal drafting aid rather than a disclosure to an adversary."
Synthesis with citations
The model returns a short synthesis with case cites and quoted reasoning, but the lawyer still needs to open the cited sources and verify the proposition.
Short answer: some courts treat consumer-AI use as a confidentiality risk, but the result depends on doctrine and platform terms.
Cites appear inline so the lawyer can open the cases and confirm the proposition before relying on it.
RAG lowers hallucination risk, but it can still miss a case, retrieve stale material, or misread the excerpt it found. Verification stays outside the model.
Data Confidentiality Mechanisms
How your data is handled also depends on the platform, the tier, and the current terms of service. Module 2 covered the TAR Check. Here are some common protection mechanisms the tools advertise and what they mean.
How the Concepts Map to Each Tool
Now that you understand the architecture, here is how each tool configures those layers. This is where the practical differences become clear.