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Law prompt of the day

November 2, 2026 · for Solo & small-firm attorneys, litigation & transactional associates, in-house counsel. One sharp, copy-paste prompt — free, every weekday.

Legal Research & MemosFREE

Opposing-argument map: the strongest case the other side will make, before they make it

Your brief is drafted and reads well to you. Build the other side's best argument against it so the gaps show up in your draft, not in their response.

You are a litigation associate preparing an internal opposing-argument map for a licensed attorney's review — work product and a drafting aid, not legal advice. I will paste our argument outline and the anonymized key facts.

Produce:

A) THEIR BEST THEORY — the strongest overall position the other side could take, in 3 sentences, stated as they would state it.

B) ARGUMENT-BY-ARGUMENT — for each of our points: their most likely response, the facts in our own record they would use, and how exposed our point is (strong / contestable / weak).

C) AUTHORITY NEEDS — for each contested point, the type of authority each side would need, written as research questions ("RESEARCH NEEDED: [what to search]"), not as named cases.

D) FIXES — where our draft should concede, reframe, or add support, ranked by how much each change reduces exposure.

Inputs: [PASTE OUR ARGUMENT OUTLINE] · [ANONYMIZED KEY FACTS] · [PROCEDURAL POSTURE + JURISDICTION] · [WHAT WE KNOW OF THE OTHER SIDE'S POSITION]

Rules: Anonymize before you paste — use role labels for parties and keep privileged strategy and confidential client information out of a consumer LLM (ABA Formal Op. 512). Do not invent cases, holdings, or record facts — authority needs are research questions, never citations. Verify every authority the attorney later finds against the official reporter and a citator before it appears in any filing. This is a drafting aid; the attorney owns the strategy.

Why it works — Drafters read their own briefs charitably, so the weakest point is the one they see least. Writing the other side's best theory first, then answering each of our points as opposing counsel would, surfaces exposure while it can still be fixed. Expressing authority needs as research questions rather than case names removes the fabricated-citation risk that has drawn court sanctions.

What changed for Law

Towards Understanding Score-Seeking in LLMs

Production large language models sometimes reward-hack, exploiting flaws in how tasks are scored rather than performing the tasks as intended, sometimes in highly sophisticated and adversarial ways.…

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On the Origin and Evolution of Training-Inference Mismatch in LLM Reinforcement Learning

On-policy reinforcement learning for large language models assumes that training responses are sampled from the current policy. In practice, an inference engine samples the responses and a separate…

arxiv

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