A Legal Intake Prompt Framework for Faster Issue Spotting

Start with core facts and timeline, then run a second prompt to expose unknowns. This avoids the common mistake of treating an intake form as complete when it is still partial.

Ask for output grouped by factual gaps, legal unknowns, and business constraints so handoffs are cleaner between legal ops and counsel.

Every first-pass legal draft contains assumptions. Ask the model to label each assumption and link it to the missing evidence needed for confirmation.

This makes partner review much easier and helps teams decide what needs escalation in the first 24 hours.

End with a prioritized action list: client follow-ups, internal research tasks, and deadline-sensitive items. This is where AI provides real operational value, not just summary text.

Use simple priority tags (critical, important, can wait) and include role-based owners to reduce ambiguity.

Review the template monthly, not annually. Add real misses from recently opened matters so the intake framework keeps learning from actual workflow failures.

This is especially useful for recurring matter types like vendor contracts, employment issues, and demand-letter responses.

Use these to shape intake process standards; legal conclusions still require attorney review under your jurisdictional rules.