AI in a lawyer's work isn't an exciting, almost futuristic experiment — it's a tool that significantly saves time, helps structure information, and solves complex tasks. With well-crafted prompts, neural networks can analyze long contracts, spot their weak points, track changes in legislation, and draft court documents.
Using the tools available on GPTunneL as examples, let's break down 10 effective prompts for different tasks a neural network can handle for lawyers.
Task 1: Extracting key contract terms (term, penalty, jurisdiction)
- Model: Claude
- Document: Expodat — a public offer agreement (PDF)
Prompt:
"Analyze the contract at this link. Extract: (a) the term, (b) penalty clauses, (c) applicable law. Build a table: Provision | Clause | Comment | Compliance with the relevant civil-code contract provisions."
Result
Claude couldn't open the link but offered options to work around the issue. After the text was pasted directly into the chat, the model highlighted the contract's term, penalty and default-interest data (noting they were absent), the contract's legal basis, and the parties' jurisdiction. The AI also proposed a table summarizing the key risks.
You can review Claude's result here.
Task 2: Identifying and classifying contract risks
- Model: GPT-5
- Document: A services agreement from a services company (PDF)
Prompt:
"Read the contract. Find provisions that pose risks for the client: unilateral changes, weak or missing penalty clauses, unspecified deadlines. For each risk, explain the cause and suggest legally sound alternative wording."
Result
After a full analysis of the contract, GPT-5 identified three risk groups — unilateral adjustment of terms, unclear liability of the parties, and vague deadlines. The AI also proposed wording that could smooth out the contentious points.
Read the full report here.
Task 3: Comparing two contract options
Model: Perplexity Sonar Pro
Documents:
- Option A — an employment-center agreement (PDF)
- Option B — an information-services agreement (PDF)
Prompt:
"Compare the two contracts across four points — term, liability, termination, force majeure. For each section, note the differences and assess which option better protects the client's interests. Build a table: Point | Option A | Option B | Lawyer's comment."
Result
Perplexity Sonar Pro analyzed the documents line by line and produced a detailed comparison table covering the main criteria:
- term;
- liability;
- termination;
- force majeure.
The model recommended revisions and provided references to the legal provisions supporting its conclusions.
For a closer look at the result, check out this conversation.
Task 4: Drafting a pre-litigation demand letter
- Model: DeepSeek V3.2
- Document: A sample demand-letter template (DOC)
Prompt:
"Based on the contract and the attached file, draft a pre-litigation demand letter:
- introduction (circumstances);
- references to the relevant contract-law provisions;
- the demand and deadline for compliance;
- a warning about referral to court.
Prepare the text in formal business style, ready to send to the counterparty."
Result
DeepSeek V3.2 generated a demand-letter structure compliant with standard legal norms.
The online AI legal assistant noted that not all attached files could be read, but offered ways to work around that. The result was a template fully aligned with business-correspondence standards: references to the relevant legal provisions, a section with demands and deadlines, and attachments (contract, evidence, payment records, penalty calculation).
You can evaluate the demand-letter draft here.
Task 5: Drafting a new contract for specific deal parameters
- Model: Qwen 3 MAX
- Document: created automatically (input data from the prompt)
Prompt:
"Prepare a draft services agreement: party 1 — an individual, party 2 — a sole proprietor, term 12 months, monthly payment, no insurance. Applicable law — the relevant local civil code. Sections: subject matter, terms, obligations, payments, liability, termination, confidentiality, force majeure."
Result
Qwen 3 MAX produced a standard services agreement between an individual and a sole proprietor. The text is based on and fully aligned with standard services-contract provisions. The online AI tool for lawyers included the following in the document:
- a structure of 9 sections, including an appendix describing the services provided;
- the contract's term;
- payment procedure;
- liability;
- confidentiality;
- termination.
If you'd like to evaluate the template, take a look at the conversation.
Task 6: Checking consistency of delivery and acceptance deadlines
- Model: Grok 4
- Document: A paid services agreement (PDF)
Prompt:
"Extract the delivery, acceptance, and claim-period deadlines from the contract. Check for contradictions between them. Assess the risks and suggest consistent wording. Output a table: Deadline | Clause | Note | Recommendation."
Result
Grok 4 fully analyzed the provided template and identified all the key deadlines:
- service delivery;
- correction of defects;
- acceptance;
- the period during which a claim can be filed.
The model determined there were no direct contradictions, but that blank fields could trigger disputes and delays. To address this, Grok 4 offered a number of useful recommendations.
You can review these recommendations and the full report here.
Task 7: Monitoring legislative changes that affect contracts
- Model: Mistral Medium 3
- Document: a hypothetical supply agreement (dated 15.01.2023, term — 24 months)
Prompt:
"Analyze which legislative changes over 2024–2025 might affect existing supply agreements. For each act, specify:
- the act's number and date;
- the essence of the change;
- which contracts it applies to;
- recommendations for the lawyer.
Present the result in a table: Legal act | Change | Risks | Recommendations."
Result
Mistral Medium 3 built a table covering 6 key legislative acts that could affect previously signed supply agreements. For each provision, it listed risks and recommendations — for example, switching to electronic document workflow or revising how penalties are calculated.
To review the model's work, check out this chat.
Task 8: Analyzing case law by dispute type
- Model: Gemini 2.5 Pro
- Document: A commercial court ruling on a debt-collection dispute (PDF)
Prompt:
"Analyze the court ruling. Identify the legal provisions applied, the court's position, and practical takeaways for drafting contracts. Prepare a brief analytical report (up to 300 words)."
Result
Gemini 2.5 Pro performed a thorough legal analysis of a ruling on a dispute over debt and penalty recovery under a supply agreement. Concluding that the ruling was well-founded, the model cited the relevant contract-law and payment-law provisions it relied on. The AI explained why the court denied the plaintiff's motion and offered recommendations on how to avoid a similar situation with other clients.
If you'd like to see the case details and the full AI analysis, check out this chat.
Task 9: Checklist for reviewing a services agreement
- Model: GigaChat Max
- Document: a hypothetical services agreement
Prompt:
"Build a structured checklist for a lawyer reviewing a services agreement. List what to check, possible risks, and recommendations. Break it into thematic sections (subject matter, terms, payment, liability, termination, confidentiality)."
Result
GigaChat Max produced a detailed checklist split into the 6 sections requested in the prompt:
- subject matter of the contract;
- service terms;
- payment;
- liability;
- termination;
- confidentiality.
For each section, the model wrote three blocks: what needs to be checked, what risks the parties might face, and practical recommendations.
You can review the structured checklist here.
Task 10: Predicting the outcome of a court dispute (win probability estimate)
- Model: YaGPT 5.1 Pro
- Document: A hypothetical acceptance report with objections and a construction contract with a penalty clause
Prompt:
"Analyze a scenario under a construction contract: the contractor missed the work deadline by 45 days. Identify the client's and contractor's arguments, possible legal risks, and the probability of winning the case. Give a quantitative probability estimate (in %)."
Result
YaGPT 5.1 Pro analyzed the scenario and highlighted the plaintiff's and defendant's arguments in its report. The model also provided references to the relevant civil-law provisions and flagged litigation risks. In addition, the AI estimated that with a strong body of evidence, the plaintiff's probability of winning is 70–85%. In the worst case — around 50%.
You can review the example here.
Conclusion
AI for lawyers and attorneys is a useful assistant that can:
- quickly extract key contract terms;
- compare several contracts and structure conclusions;
- assess the probability of court outcomes;
- prepare a standard contract or pre-litigation demand letter;
- analyze rulings that have already been issued;
- handle many other tasks.
The key is to set the task correctly — and afterward, double-check the information provided.