- GLM Legal examples cover five core workflows: contract review, evidence analysis, research, compliance, and drafting
- Every workflow needs defined task goals, clean input materials, and structured output requirements
- All outputs require professional human review before use in practice
- Prompts should always specify jurisdiction, citation rules, and output schemas
- Benchmark scores measure task performance, not legal service quality
What GLM Legal Examples Show in Practice
GLM Legal examples demonstrate how a legal-focused GLM model supports professional workflows. Each example pairs a clearly defined legal task with supplied source materials, a required output format, and a mandatory human review step. The model organizes complex materials, identifies relevant issues, and generates structured first-pass work products, while final judgment remains with qualified professionals.
Contract Review
- Identify obligations, unusual clauses, and risks
- Structured clause summaries and issue lists
- Lawyer verifies interpretation and revisions
Evidence Analysis
- Build fact matrices and timelines
- Map contradictions across documents
- Team confirms provenance and sources
Legal Drafting
- Generate structured first drafts
- Follow approved templates and defined terms
- Responsible lawyer approves final language
GLM-Legal is a legal AI assistant, not a replacement for a lawyer. Treat every output as work-product assistance that a qualified professional must review and approve before it is used in practice.
Five Core GLM Legal Example Workflows
The five workflows below define what each GLM Legal example produces. Each row summarizes the task goal, required inputs, output focus, and the review boundary.
| Workflow | Task Goal | Input Materials | Output Focus | Human Review |
|---|---|---|---|---|
| Contract review | Spot key obligations, unusual clauses, risks | Contract text, exhibits, playbooks | Clause summary, issue list, risk classification | Lawyer verifies interpretation, business context |
| Evidence analysis | Organize facts and document relationships | Chronologies, emails, pleadings | Fact matrix, timeline, contradiction list | Team confirms evidence, preserves provenance |
| Legal research | Structure questions, summarize authorities | Statutes, cases, research notes | Issue tree, authority summaries, outline | Professional checks validity, jurisdiction, quotes |
| Compliance planning | Convert requirements into controls | Rules, policies, risk registers | Requirement matrix, control checklist | Compliance team validates applicability |
| Legal drafting | Create structured drafts or reformat content | Templates, facts, defined terms | Outline, draft language, open issues | Lawyer revises and approves final language |
Remove irrelevant documents and duplicate versions before testing. Label titles, dates, parties, and versions, and protect confidential, personal, and privileged information before any materials enter a model workflow.
GLM Legal Example Prompts That Work
Reliable legal prompts separate the factual record from the requested task, define the legal context first, and demand citations for every conclusion. The patterns below are reusable starting points.
| Prompt Pattern | Purpose | Key Elements |
|---|---|---|
| Contract review | Find material risks a lawyer can verify | Background facts, review priority, clause citations, confidence levels |
| Evidence organization | Build a traceable chronology | Matter background, source identifiers, inferred dates labeled, contradictions preserved |
| Compliance checklist | Auditable first-pass control map | Regulation scope, evidence requirements, responsible roles, gap actions |
| Quality controls | Stabilize any legal prompt | Restate task, separate facts from assumptions, fixed schema, escalation field |
Define background facts
State contract type, parties, business objective, applicable jurisdiction, and review priority before asking anything.
Specify the task and output schema
Number the required outputs, for example: clause reference, issue category, plain-English explanation, impact, follow-up action, confidence.
Set citation rules
Require a clause or page citation for every finding and forbid introducing facts not present in the supplied text.
Add uncertainty handling
Ask the model to list missing facts, unresolved issues, and assumptions separately from source-supported conclusions.
Use a fixed output schema such as: Issue ID, Clause reference, Risk category, Finding, Potential impact, Follow-up action, Confidence. Consistent schemas make outputs easier to review at scale.
How to Run Your First GLM Legal Example Test
A good first task is narrow, document-based, and easy to verify. Start with one defined legal question and a limited set of materials.
Understand the product
Review the GLM-Legal overview and identify the legal task, intended users, reviewers, and a clear success criterion.
Apply for access
Use the official legal consultation channel to discuss availability, deployment expectations, and integration requirements.
Prepare materials
Assemble clean, authorized documents; remove duplicates; label versions; protect confidential information.
Design a focused test
State the objective, specify the output structure, and ask the model to flag missing facts.
Verify facts and citations
Check every factual statement, citation, date, and party name against original sources.
Complete professional review
A qualified lawyer confirms jurisdiction, applicability, and business context before any output is used.
First Test Readiness:
- Legal task and success criterion defined
- Materials cleaned, labeled, and authorized
- Prompt specifies jurisdiction and output schema
- Citation and uncertainty rules included
- Reviewer assigned for final approval
Separate source-supported conclusions from assumptions in every review pass. Record corrections and improve the workflow before expanding to more complex tasks.
Benchmarks, Models, and Responsible Use
GLM-Legal is evaluated through benchmarks such as Harvey LAB, LegalBench, and PLawBench. Each measures different task types and evaluation conditions, and scores should be read in context rather than treated as a measure of legal service quality.
| Evaluation | What It Measures | Key Limitation |
|---|---|---|
| Harvey LAB | Legal-domain reasoning and work-oriented tasks | Bounded by selected tasks, prompts, jurisdictions, scoring |
| LegalBench | Task-specific legal reasoning across problem types | May miss client context, evolving law, procedural strategy |
| PLawBench | Legal understanding and reasoning benchmarks | Varies with language, jurisdiction, prompt design |
| Cross-benchmark | Coverage comparison across frameworks | Scores not interchangeable across datasets and setups |
Benchmark performance is useful for comparing model behavior under controlled conditions. The most reliable evaluation combines benchmark results with error analysis and testing on your organization's own documents.
FAQ
Q: What are the main GLM Legal examples in practice?
The five core GLM Legal examples are contract review, evidence analysis, legal research, compliance planning, and legal drafting. Each defines a task goal, required input materials, a structured output, and a human review step.
Q: Can GLM Legal outputs be used without a lawyer's review?
No. Every GLM Legal example treats the output as work-product assistance. A qualified lawyer or responsible legal professional should verify facts, citations, jurisdiction, and business context before any material is used in practice.
Q: How should I structure a GLM Legal prompt?
Separate background facts from the task, define the jurisdiction and source set, require clause or page citations for every finding, use a fixed output schema, and ask the model to flag missing facts and assumptions separately.
Q: Do benchmark scores prove legal quality?
No. Benchmarks like Harvey LAB, LegalBench, and PLawBench measure performance on defined tasks under controlled conditions. They do not replace professional judgment or validation on your own documents.