27 Feb 2026

Context Windows In Legal AI And Why Content Still Determines Quality

By Greg Dickason, Chief Technology Officer, LexisNexis

Legal teams ask a practical question. If large language models are so capable, why does legal AI still depend on curated content, and why does surfacing that content matter so much?

Context windows in legal AI are a key part of the answer. A model can only consider a finite amount of text in a single run. If the system passes too much material, the model can miss what controls. If the system passes too little, it can miss the facts and procedural posture that make an authority relevant.

The result is not a cosmetic error. It is the difference between an answer that reads well and an answer that holds up in a filing or in front of a judge.

Why Legal Context Is Harder

In law, there is a lot of conflicting material. A legal AI system can struggle to separate arguments inside an opinion from the holding, distinguish dicta from binding reasoning, or explain why one judgment carries more weight than another on a specific issue in a specific jurisdiction.

Lawyers do this instinctively. They identify controlling authority, focus on the key facts, and connect reasoning to the client’s posture and forum.

That nuance is the point. The right authority has to match the right proposition, in the right court, at the right stage, for the right purpose. Facts are not just facts. They are facts anchored in precedent that a judge will respect.

So yes, content matters. Serving the right content at the right moment matters more.

Context Windows In Legal AI Limits

Large language models have a context window, often described as working memory. Performance can drop as inputs get longer. Information placed in the middle of a long prompt can be missed even when it is present, a pattern studied as “lost in the middle” (Lost in the Middle).

Recent work also describes “context rot,” where model reliability varies across input length as token counts rise (Context Rot).

Bigger context windows help, yet they do not solve relevance. If retrieval pulls in extra documents, the model can overweight what it sees last, or rely on a persuasive passage that is not controlling for the question at hand (AI’s limitations in the practice of law).

That is why more text is not automatically better. Legal AI needs the right amount of information, well linked, well structured, and tightly relevant to jurisdiction, issue, and procedural posture.

How Curated Legal Content Helps

One way to manage context is to build deeply connected legal data. Citations, authorities, treatment, procedural posture, jurisdictions, judges, issues, and the relationships between them can be encoded and updated as new decisions arrive.

That work is continuous. A new ruling can reshape an argument, shift how precedent is read, or change what courts treat as persuasive. Context selection has to adapt at the same pace.

This goes beyond a generic model provider, or a thin wrapper over an API. It requires sustained editorial and engineering discipline: ingesting raw material, normalizing it, linking it, validating it, tracking treatment over time, mapping facts to issues, and surfacing only what is relevant for a given question.

Why Context Precision Drives Workflows

Correct context powers more than research. It supports workflows, the steps lawyers must take under procedural rules that vary by court and can impose hard deadlines.

Take a motion to dismiss. You need authority to support the substantive argument. You need the process the court will follow: standards, local rules, page limits, required components, and timing. If the system misses one of those constraints, the output can be unusable when the legal discussion sounds plausible.

Much of that material is not reliably available on the open web in a form that is complete, current, and actionable. Building and maintaining it requires investment over time.

Practical Context Engineering Signals

In practice, high-quality context management in legal AI often depends on:

Source fidelity: every citation resolves to an authoritative source.

Structured connections: authority, treatment, posture, and jurisdiction are encoded, not guessed.

Relevance filters: retrieval narrows to what controls, not what is merely related.

Continuous updates: new decisions and amendments refresh links and metadata.

Right-sized prompts: the model sees what it needs, no more.

Where To Go From Here

Context windows in legal AI are a real constraint. Long or poorly scoped inputs can cause models to miss controlling authority, misread what matters, or fail to connect precedent to the facts that make it relevant.

That is why output quality depends on content quality and content structure such as authoritative sources, jurisdictional specificity, and citation-grounded relationships delivered in the right scope for the question.

Evaluate legal AI on whether it can surface controlling law, explain application, and show its work with citations that stand up to review.

To learn more about how Lexis+® with Protégé™ can work for you, book a demo today.