Bad prompts yield bad legal analysis. I learned this the hard way. The exact XML formatting structure below is what forces Claude to analyze 50-page PDFs deterministically.
A law firm cannot rely on basic conversational prompts like "Review this contract for risks." The LLM will give you a generic, useless summary. You need deterministic XML framing. This is exactly what business AI agents deliver : structured, reliable output every time.
The problem: Without XML boundaries, Claude treats the entire document as one blob. It loses track of what's instruction and what's contract text. The output becomes unreliable.
The Prompt Engineering Baseline
Anthropic's models (Claude 3.5 Sonnet / Opus) are heavily trained on XML tags. If you don't structure your system prompts with XML boundaries, the model loses context on dense 50-page legal PDFs.
The Architecture of a Legal Prompt
Here is the exact prompt structure to deploy an autonomous contract reviewer.
<system_instructions>
You are a Senior Corporate Attorney specializing in SaaS Master Services Agreements.
Your task is to identify critical business risks in the attached contract.
</system_instructions>
<rules>
1. Flag any auto-renewal clauses.
2. Flag any limitation of liability caps below $1,000,000.
3. Quote the exact text of the flagged clause.
4. Output your findings strictly in the provided JSON schema.
</rules>
<contract_text>
[INJECT RAW TEXT HERE]
</contract_text>This structure guarantees operations stability because it forces the model to treat the instructions and the raw data as two distinct entities. That prevents prompt injection and hallucination bleed. See how this pattern scales in our case studies.
The key insight: Separate instructions from data using XML tags. Claude respects these boundaries. Without them, you're gambling on output quality.
Ready to integrate this into your workflow? Download the Blueprint or AI Workflow Repair Intake. For a complete legal automation pipeline, see how Make.com automates document drafting and CRM API workflow overhaul eliminates manual data entry.