First-Draft Contract Review on Claude Opus 5.5

01 · What the first-draft pass actually covers in a legal or compliance queue

Legal and compliance teams rarely start from a blank page. A first-draft review is the machine-assisted pass that runs before a human reviewer opens the matter: the model reads the agreement or submission, marks the clauses that deviate from the playbook, lists the obligations the counterparty is accepting, and writes a plain-language summary for the requesting business team. It does not decide anything. It prepares the ground so a lawyer, a compliance officer or a contracts manager spends time on judgment instead of on retrieval.

In practice, a well-scoped first-draft pass covers four kinds of output:

• › Deviation flags against your standard positions: indemnity caps, liability carve-outs, governing law, termination for convenience, and data-processing terms.
• › Obligation mapping: what the counterparty must deliver, when, and which internal policy or control each commitment touches.
• › Open questions for the requesting team, written in language a product manager or procurement lead can answer without a legal translator.
• › Risk summaries that rank the unusual items so the human reviewer knows where to start reading.

None of these outputs is final. Every item flows to a named human reviewer, and the model's text stays clearly labeled as draft material inside your matter record. That boundary matters more than any prompt trick: it is what keeps the process defensible when an audit, a regulator or a dispute asks who reviewed what and when.

02 · Choosing between Claude Opus 5.5 and a fast triage model by review tier

Before you standardize on one model for everything, sort your intake by consequence. A tier-one master services agreement with security, liability and IP terms deserves a different treatment than a low-variance NDA that matches your template in ninety percent of clauses. Teams that push both through the same pipeline either overpay on the routine file or under-process the risky one.

For high-stakes drafts, a top-tier reasoning model such as Claude Opus 5.5 is the sensible default. It holds a long agreement and a long playbook in context at the same time, which is exactly what deviation analysis needs: the file, the fallback positions, and the negotiation history all have to sit in the model's working window at once.

For triage, a fast and inexpensive model such as Gemini 2.5 Flash is enough to classify intake, extract parties and dates, and route the matter to the right reviewer. Keep the fast tier for sorting and summarizing; keep the heavy tier for anything where a missed clause has a commercial or regulatory consequence.

• › Route by clause complexity and contract value, not by who asked.
• › Keep one written rule for when a fast-tier result must be escalated to a heavier pass.
• › Record which tier produced each first draft, so quality reviews can compare like with like.

03 · Privilege, confidentiality and grounding rules for the first-draft pass

Model-assisted review only works if your existing legal and compliance obligations survive contact with it. Start with data handling. Decide what may leave your systems, redact personal data and privileged material before anything is sent, and document that decision in the same policy that governs outside counsel and e-discovery tools. Do not assume vendor terms equal training consent; write the rule down and enforce it technically.

Then move to grounding. A first-draft review is only trustworthy when the model quotes or points to the clause it is discussing. Give it the source document plus your playbook, and require every flag to cite the clause it refers to. Anything the model cannot point to goes into an open-questions list rather than into a conclusion. Hallucinated clause numbers are a real failure mode, and the cheapest control is a requirement that every flag carries a location in the source.

Finally, keep the audit trail. Store the inputs, the model and tier used, the draft output, and the human edits alongside each other. When a compliance officer later asks why a fallback position was accepted, the answer should be reconstructable from the record alone, without relying on anyone's memory of a chat window.

04 · Token metering, cache reads and cache writes in a sustained review queue

First-draft review at volume is a sustained workload, not a burst. A team clearing a steady queue of agreements re-reads the same playbook, the same templates and often the same counterparty history on every call. That repetition is where your cost model either works for you or against you.

Budget in tokens, not in seats. Every call is metered on real tokens consumed, and prompt-caching terms should be modeled explicitly: cache writes and cache reads are priced separately, so a stable system prompt and playbook that are cached on the first call and read on later calls behave very differently from a pipeline that rewrites its instructions every time. Structure prompts so that the fixed part — policies, playbook, definitions — sits at the front and stays stable, and only the document under review varies.

This is the workload profile that relays and shared balances are built for. Quanta is a pay-as-you-go API relay for power users and teams with heavy, sustained usage: one balance covers the major model families behind a single interface, it works with the coding agents and OpenAI-compatible SDKs your engineers already use, and every call is metered on real tokens with cache reads and writes itemized. For a legal team, that means the budget conversation becomes a per-matter line item instead of a stack of per-vendor invoices.

• › Set a monthly token ceiling per team and per matter type.
• › Alert before the ceiling, not after the invoice.
• › Review cache hit behavior quarterly; it is usually the largest controllable lever.

Open a Quanta account when you are ready to run the pilot on one balance and meter the workload properly; teams that refer other teams also earn a share of their top-ups.

05 · Rollout steps for a team lead in the first quarter

A first-draft review rollout fails when it is launched as an experiment without a definition of done. Scope it narrowly first: one agreement type, one playbook, one reviewer group, and a fixed evaluation set drawn from matters that have already been closed. Score the model's output against the decisions your best reviewers actually made — deviation flags caught, false positives raised, time to a reviewable draft.

Run the pilot in shadow mode before it touches anything external: the model prepares drafts, humans treat them as one input among several, and you compare the difference. Publish the rubric internally so reviewers score the same way. Then widen scope one agreement type at a time, keeping the heavier tier for consequential files and the fast tier for triage.

• › Week one to two: define intake tiers, redaction rules, and the grounding requirement that every flag points to a clause.
• › Week three to six: shadow-mode pilot on one agreement type, with weekly reviewer calibration.
• › Week seven onward: publish token budgets and per-matter cost visibility, then extend to the next agreement type.

Keep three limits non-negotiable throughout: no matter closes without named human sign-off, no source leaves your approved boundary, and no model output is stored as final review documentation. With those in place, a model-assisted first-draft pass stops being a novelty and becomes another documented step in a review chain your auditors already understand — one that happens to run on tokens, cache reads and a single metered balance.