Comparison
A decision guide that maps when to use purpose-built legal AI tools versus general-purpose foundation models — and what each is actually good for — so the team reaches for the right engine the first time: specialized tools for governed, playbook-bound work, and general models for exploration, synthesis, and first drafts that an attorney then reviews.
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A decision guide for when a purpose-built legal tool earns its cost and when a general model is the right answer — judged on grounding, confidentiality posture, workflow fit, and reviewability.
It saves teams from buying a platform for a task a general model handles, and from using a general model where citations must hold up.
01
Tuned and grounded tools for defined legal tasks — clause review against a fallback position, precedent retrieval with verified citations, and ROPA drafting — best when the work is playbook-bound, repeatable, and needs auditability and a defensible review trail.
02
Open-ended models for exploration and synthesis — issue spotting on novel fact patterns, first-draft framing, and plain-language explanations — best when the task is judgment-heavy, non-repeating, and a human attorney will review and rework the output before it leaves the team.
03
Specialized tools ground answers in a controlled corpus with source links a reviewer can open; general models generate fluent text that can hallucinate citations — so treat any unverified citation from a general model as untrustworthy until checked against the primary source.
04
Defines which engine may see what: specialized legal tools with enterprise data isolation for matter-bound work versus de-identified or redacted prompts to general models for anything touching client confidences, with a firm-approved list rather than attorney-by-attorney choice.
05
Specialized tools slot into existing review steps with built-in redlining and sign-off; general models produce a draft that must enter the standard review queue — so the workflow, not the model, controls what reaches a counterparty or the file.
06
Gives the team a one-page rule: task type, confidentiality tier, and audit requirement together pick the engine, so 'which AI do I use here?' has a documented answer instead of a guess.
The inputs that make the output useful. Missing any of these usually shows up as a vague result.
Representative outputs, with illustrative examples. Every output is reviewed by a qualified professional before it is relied on.
Specialized tool, general model, or manual — with reasoning.
Case-law research → specialized tool (citation grounding). Meeting summaries → general model.
Side-by-side on grounding, confidentiality, cost, and workflow fit.
Grounding: specialized = cited corpus; general = unverified unless supplied.
A short policy line the team can apply without re-deciding each time.
No external filing may cite a general-model output without independent verification.
We tailor each tool to your playbooks, thresholds, and review requirements before it goes live.
