Introduction: Beyond the Prompt

For the past year, everyone in tech and legal circles has been buzzing about prompt engineering. Crafting the perfect ChatGPT prompt became the trendy new skill – akin to casting the right spell to get magical results. But as we’ve poured our attention into prompts, we may have taken our eye off the ball. The real game-changer for generative AI isn’t just how we phrase the question – it’s how we frame the entire exchange. In other words, it’s all about context. In the not-so-distant future, most end-users might not even see raw prompts at all; they’ll just click a button or ask a voice assistant, and a behind-the-scenes agent will handle the rest. Prompt engineering will still matter, but it’s quickly becoming just one piece of a larger puzzle. That larger puzzle is context engineering – and it’s where the real magic sauce lies for leveraging GenAI effectively, especially in knowledge-driven fields like law.

As a legal professional (or someone working with them), why should you care about context engineering? Because in practice, context is king. Even the most advanced AI model can fumble if it’s working with the wrong or insufficient context. Conversely, a mediocre model can produce surprisingly useful output when fed rich, relevant context. If you’ve found generative AI disappointing or “hallucination-prone” in your legal work, there’s a good chance the issue wasn’t the model at all – it was the lack of proper context. This article will demystify context engineering in an accessible way and explain why optimizing context is critical for legal use cases. We’ll explore what context engineering means, how it differs from prompt tinkering, and how it will impact everything from contract drafting to legal research in tools like Harvey, NewCode.ai, Legora, or Microsoft 365 Copilot.

(As someone who studied English and Journalism, I have a special love for words and context. In a sense, context engineering lets my wordsmith’s instinct shine in the AI realm.)

From Prompt Engineering to Context Engineering

Not long ago, being a “prompt whisperer” felt like the key to unlocking AI’s potential. A well-crafted prompt – clear instructions, the right tone, maybe even a clever trick – can indeed coax better answers from an AI. Prompt engineering is about figuring out what to ask and how to ask it. But it has limits. Think of prompt engineering as asking a single brilliant question to a very smart but forgetful person. What happens if that person doesn’t have the documents, facts, or background needed to answer you? No matter how eloquent your question, the answer may miss the mark.

This is where context engineering takes over. While prompt engineering is about asking the right question, context engineering is about making sure the AI has the right knowledge and environment to answer that question. Instead of treating the AI like a “very new employee with amnesia” who only sees your one prompt, context engineering gives the AI something closer to a working memory. It involves building dynamic systems that supply all the relevant information, tools, and history to the model automatically. In short, prompt engineering produces a well-phrased question; context engineering sets the stage on which that question is answered.

Crucially, context engineering recognizes that the interaction with AI can be stateful and expansive. Rather than a one-off query, many real tasks involve multiple steps or a conversation. Context isn’t static – it may evolve over the course of a dialogue, or new information may need to be fetched and inserted at any time. For example, if an AI agent breaks a task into sub-tasks with different prompts, all sub-agents must share the same overarching context (facts, goals, constraints) to work coherently. Likewise, modern AI applications maintain a context window – a limited “memory” of recent dialogue or provided data. Engineering within this window means deciding what information to keep, what to trim, and how to format it so the model focuses on what matters. As one expert succinctly put it: “Provide complete and consistent context – don’t make the model guess missing pieces.”

In practical terms, context engineering encompasses techniques like Retrieval Augmented Generation (RAG), where the system fetches relevant documents or facts on the fly and feeds them into the prompt. It also covers using memory buffers or conversation history so the AI remembers what was said earlier, rather than starting from scratch every turn. It includes formatting data or instructions in ways the AI can easily parse – for instance, telling the model explicitly how to use the provided information (to avoid it getting ignored or misused). Context engineering even involves guardrails: making sure that when you give the model a lot of information or tools to use, you also guide it to use them safely and correctly. In essence, context engineering is a holistic approach: instead of just one prompt, you’re orchestrating an entire supporting cast of data and directives around the AI to get the best outcome.

What Exactly Is “Context” in Generative AI?

Before diving deeper into why this matters for law, let’s clearly define context in the realm of AI. Context is all the information that an AI model has available when generating its output, beyond the basic prompt itself. It can include things like:

• Background text or data: For example, the content of a contract you want analyzed, a set of facts about a case, or excerpts from relevant laws.
• Instructions and examples: You might provide guidelines (“Answer in a formal tone”) or show the AI a few examples of the kind of output you want. These also form part of the context.
• Previous conversation history: In a chat, the model’s replies and your earlier questions are context that influence subsequent answers.
• Tool outputs or external knowledge: If the AI can call a database or search tool, the results brought back become context for the next step.

In technical terms, modern language models operate within a context window, which is like the model’s working memory for a given interaction. Everything you cram into that window (up to a certain limit of tokens/words) is what the model “sees” and uses to produce its answer. If something isn’t in that window – either in the prompt or embedded context – the model has to rely on whatever it “learned” during training (which might be outdated or not specific to your query). Context engineering is the art and science of filling that window with the most relevant, useful information, in the most effective format. It asks not just “How do I word the question?” but “What background data and cues do I provide so the AI can do its job right?”.

To illustrate, think of an AI like a junior lawyer or paralegal assisting you. Prompt engineering is giving that assistant a nicely phrased question or task. Context engineering is handing them the case file, reference books, and a checklist of what you need. No matter how intelligent your assistant, if you only give a vague instruction with no files or context, you’ll get a vague answer. But if you drop them into a room full of organized binders and guidelines, you set them up to excel. As Phil Schmid summarized, “Context engineering is the discipline of designing and building dynamic systems that provide the right information and tools, in the right format, at the right time for LLMs and AI agents.” It’s about designing the AI’s entire information ecosystem – from knowledge databases and retrieval pipelines to conversation memory – not just the query wording.

In practice, context engineering can involve quite technical strategies (like using vector databases for semantic search, chaining prompts together, or fine-tuning models to better use provided context). But you don’t need to be an AI engineer to grasp the core idea: before the AI gives you good answers, you must give the AI the right information. That could be as simple as supplying a relevant document or as complex as an automated system that plucks facts from various sources. Either way, the quality of the AI’s output is a direct reflection of the quality of context you provide. Garbage in, garbage out – or conversely, useful context in, useful answers out.

Why Context Engineering Matters (Especially in Legal)

We’ve hinted at this already, but it bears repeating: the gap between mediocre and meaningful AI results often comes down to context. Generative AI without sufficient context is like a lawyer without a law library or a client file – they might default to generic statements, make incorrect assumptions, or outright fabricate plausible-sounding answers (the dreaded hallucinations). When tasks get complex or domain-specific (e.g. legal analysis, drafting a contract, researching case law), a cleverly worded prompt alone won’t save you if the model isn’t grounded in the right knowledge. As one AI expert bluntly noted, “LLMs will faithfully use whatever you provide in context, so you must curate it well”. In other words, if you feed the model too little or too much, you’ll get poor results. But if you feed it accurate, pertinent context, you steer it toward reliability.

This point is especially important in the legal industry, which runs on information and nuance. Law is a knowledge profession: the answer to a legal question always depends on the specifics – the jurisdiction, the facts, the contract language, the precedent. A generic answer is usually a wrong answer. If you’ve been underwhelmed by an AI tool’s legal output, ask yourself: did I give it all the context it needed? Often, the answer is no. For example, early users sometimes asked ChatGPT things like “Is this non-compete clause enforceable?” without providing the state law in question – essentially expecting the AI to answer from general training data. The AI might have given a generic response about non-competes, but nothing it says can be truly trusted without the jurisdiction. With proper context engineering, however, you’d provide the specific clause language and note the relevant state (say, California, which bans most non-competes). Armed with that context, an AI can give a far more relevant, accurate analysis.

To make this concrete, consider a scenario from a recent AI research article: “Summarize the relevant case law for this dispute.” If you pose that as a lone prompt, it’s insufficient – a large language model doesn’t magically know which dispute, which facts, which jurisdiction, or what counts as relevant. A context-engineered approach would be to first supply the AI with the context of the dispute: a summary of the case facts, the jurisdiction, perhaps the key legal questions at hand. It might also involve retrieving several on-point cases or statutes and giving the AI summaries or excerpts of those. Context engineering means selecting the most applicable documents or facts, compressing them if needed to fit the AI’s input limit, and arranging them so the model can easily reference them. Only then do you ask the AI to summarize the case law or give an opinion. The difference in output is dramatic – the AI with rich context can synthesize and cite the specific authorities, whereas the AI with just a vague prompt will either generalize or hallucinate. As that article concluded, a system with proper context “dramatically outperforms one relying on prompt engineering alone”.

Legal work is full of such examples. Drafting a contract? The AI will do much better if you provide a term sheet or outline of deal points (context) rather than just saying “draft a contract for me.” Analyzing a lengthy document? Split it into sections or summaries for the AI, so each part stays within context window limits, and maybe include an outline of the document structure. Answering a client’s question? Feed the AI the client’s question history or any prior advice given, so it doesn’t contradict yourself and understands the client’s context. Without these efforts, you’re essentially asking the model to operate in a vacuum. And when an LLM lacks real context, it’s forced to either say “I don’t know” or more often make something up based on general training – which is obviously dangerous in law. As one detailed guide observed, even the most powerful models “fail when they are fed an incomplete or inaccurate view of the world.” If the AI’s output has been underwhelming, fix the context before you blame the model. Often, “the model did not get ‘smarter’; its environment did” – meaning the quickest path to better AI results is to improve what information you give the AI, not necessarily switching to a more advanced model.

For legal professionals, context engineering will be the differentiator between AI tools that are parlor tricks and those that are indispensable daily aides. Lawyers are trained to say “it depends” because the answer always hinges on context. We should treat our AI assistants the same way – don’t ask for answers in a vacuum. Give them the context they need to mimic a seasoned lawyer’s analysis. If you find GenAI “not useful” or too generic, take a hard look at how you’re using it: are you engaging in context engineering or just typing prompts? Chances are, incorporating more context (the facts of your matter, the body of law, the specifics of your question) will take your results from shrug-worthy to significant.

Real-World Example: Context Is Key in Legal AI

Let’s illustrate the power of context with a simple, relatable scenario for a legal team. Imagine you’re a lawyer preparing a memo on whether a certain clause in a contract is enforceable. You decide to enlist a generative AI assistant in your research. You go to your AI tool of choice and type: “Is the indemnification clause in this contract enforceable?” The AI, based on its general training, might give you a general answer about what indemnification clauses are, perhaps noting some generic enforceability considerations. But it doesn’t actually know the clause or the context – you haven’t provided the contract text, any details about the parties, nor the jurisdiction. The result? A broad answer that isn’t very useful (and could even be misleading). This is GenAI with almost zero context.

Now consider a context-engineered approach. You feed the AI the text of the indemnification clause in question (perhaps by uploading the contract or copy-pasting the clause). You also tell it the governing law of the contract is, say, New York. You add that the specific concern is whether the clause covers certain kinds of damages. In other words, you surround the question with relevant data and framing. The prompt might now look like:

“Here is the indemnification clause from our contract [text of clause]. The contract is governed by New York law. The dispute is whether this clause would require our client to indemnify the other party for consequential damages arising from a breach. Analyze whether this indemnification clause is enforceable to that extent, under New York law, and explain any limitations.”

The difference in the AI’s response will be night and day. With the actual clause and situation in context, the AI can identify specific wording (e.g. does the clause explicitly mention consequential damages or not), recall or research New York law on indemnities, and give a tailored answer. This is not hypothetical – everyday users of legal AI tools see this contrast. One leading legal AI platform found that attorneys who provided sufficient context (like uploading related documents or specifying jurisdictions) got far more precise answers, while those who asked one-liner questions were often disappointed by vagueness or errors. In effect, “context is king” for useful legal AI output. A good prompt in law always includes context – the facts, the issue, and the desired form of answer – because without it, even a brilliant AI is just guessing.

Another real-world demonstration comes from how AI tools handle document review. Let’s say you have a trove of 1,000 emails and you need an AI to find any that pose a legal risk (perhaps potential privilege issues or compliance flags). If you simply prompt an AI, “Find any risky emails in this dataset,” it can’t magically do that without context – it needs to actually see the content of the emails. A context engineering approach would use a tool to feed the emails (or summaries of them) into the AI’s context window, likely in batches due to size. The AI could then analyze each chunk with full knowledge of that chunk’s content. Modern legal AI platforms do exactly this: they ingest large document sets and let you query them. For example, many platforms offer different integrations (like Azure or iManage) so that when you ask a question, the system can pull in the most relevant documents from your own files as part of the context. The AI isn’t just spewing training data; it’s actually reading your firm’s content in real time to give “specialized, context-aware insights.”

The bottom line: whether it’s analyzing a contract clause, summarizing a deposition transcript, or hunting for key points in discovery, providing the AI with the actual texts and facts to work with transforms the output. Legal professionals who treat GenAI as a knowledgeable colleague to whom they must give a case file and clear instructions will reap far more value than those who treat it as a magic 8-ball. In short, context turns a toy into a tool. It’s the difference between an AI that outputs boilerplate and one that delivers insight akin to a well-prepared associate. So the next time you use an AI assistant on a legal task, remember: if the answer you got wasn’t great, ask yourself what context was missing and try again with more context in the mix.

Preparing Your Legal Knowledge for Context-Driven AI

If context engineering is the secret sauce, then a practical question arises: how can law firms and legal teams optimize their knowledge and data to take advantage of it? In other words, how do you make sure that when AI comes knocking for context, it finds what it needs (and finds it in a useful form)? This is where some good old-fashioned information governance and “content engineering” comes into play.

Here are a few considerations to help your organization be AI-ready:

• Organize and Centralize Your Data: AI can’t use what it can’t find. Audit where your important documents, emails, and knowledge reside. While many law firms will have a DMS as the system of record, you may also have important files shared in Microsoft Teams (which are stored in SharePoint), and discovery data across on premises servers and with third-party vendors. Many firms are moving toward consolidated platforms (like a private Microsoft Azure environment) so that an AI context engine has a single, authoritative place to search. Within that, use clear taxonomies (by client, by matter, by document type) – essentially create an environment where relevant info is easy to retrieve. AI-powered search can handle a lot, but if your data is siloed or disorganized, valuable context might be missed.
• Embrace Metadata and Tags: In an ideal world, every document in your system has metadata – e.g. a document type, date, author, matter number, jurisdiction, keywords, etc. Metadata is extremely helpful for AI because it’s like a cheat sheet about the content. As one guide put it, “Think of metadata as tags or labels that help [AI tools] understand the context behind your data, making responses more accurate and relevant.” For instance, if a brief is tagged as relating to “Contract Law” and “Delaware” and “Indemnification,” an AI tool can use those tags to quickly zero in on relevant docs when you ask a question about Delaware contract indemnities. You don’t necessarily have to tag everything manually; modern tools (and diligent knowledge managers) can automate a lot of this. But it’s worth the effort – adding structure to your unstructured data is a form of context engineering that pays dividends in AI outputs.
• Clean Up and Curate Your Knowledge Base: Quality of data matters as much as quantity. Duplicates, outdated documents, or erroneous information in your system can confuse AI just as they confuse humans. It’s a great time to clean house: archive what’s old or irrelevant, flag “gold standard” documents (like model contracts or memos) that you’d actually want the AI to emulate, and scrub obvious errors. Think of it as preparing the ingredients for a recipe – fresh, well-organized ingredients make for a better meal. Some firms are even creating curated AI knowledge libraries, where they intentionally load only vetted, approved documents into the AI’s retrieval pool. That way, any context the AI pulls in is something a partner trusts, not a random associate’s half-baked memo from a decade ago. Curating context sources in this manner can reduce the risk of the AI using bad info.
• Structure Notes, Meeting Transcripts, and More: Legal practice generates a ton of textual data beyond formal documents – think meeting notes, deposition transcripts, call recordings, email threads. These are treasure troves of context if harnessed. For example, imagine having an AI that can comb through a deposition transcript and summarize key admissions, or one that can recall a specific detail from a client call last month. To enable this, start logging and storing these less-formal texts in accessible ways. Transcripts of important meetings or hearings should be saved (and even broken into smaller chunks with summaries, for easier AI digestion). Internal team notes might be moved from personal notebooks into a central, searchable system (with sensitive info controls as needed). The key is to convert ephemeral knowledge into text the AI can later use. And if you can, add a bit of structure – e.g. label sections of a transcript by witness or topic, or tag notes by client or project. Down the line, when you ask your AI assistant “What did we decide about X in last month’s client call?”, it will only be able to answer if that discussion was documented and accessible as context.
• Mind the Limits (and Use Summaries): Even as context windows grow (some AI models can take tens of thousands of words as input now), they’re not infinite. Plus, shoving too much text at once can actually degrade performance, with models losing the thread if the context is overly long or irrelevant. A good practice is to have summaries or key point outlines for long documents. For instance, before feeding a 100-page contract to an AI, consider prepping a 1-2 page summary of it. That summary can act as a high-level context, with the full text available if drilling down is needed. Likewise, maintain executive summaries for lengthy research memos or case files. Think of it as tiered context: broad strokes up front, detail on demand. This kind of summarization is actually a context engineering technique itself – condensing information to maximize the signal within the token limit. Many AI tools can auto-summarize for you; if yours can, use it to create digests that you then verify and refine.
• Security and Confidentiality First: Finally, in bringing more data into play for AI, always keep security and ethics in mind. Context engineering doesn’t mean dump everything to the AI without caution. Ensure that any sensitive client data stays within approved systems (e.g. if using a cloud AI, it should be one that’s enterprise-grade with proper encryption and not commingling your data with others). Redact what needs redacting, and use access controls so that an AI won’t accidentally include confidential context in an answer shown to the wrong person. Think of context like any other data integration – follow your organization’s data handling policies. Microsoft’s Copilot, for example, promises that your data remains within your tenant and is not used to retrain but you still should govern who can query what. In short, engineer context securely – the right info to the right AI, under the right safeguards.

By taking steps like these, you’re effectively becoming a context engineer in your own domain. You’re shaping and pruning the knowledge landscape so that AI tools can traverse it effectively. This upfront effort will mean that when AI is deployed, it can hit the ground running – yielding useful insights without you having to babysit it with constant prompt tweaks.

Conclusion: Embracing Context for the AI-Powered Legal Future

Generative AI is often described as “transformative” for legal work, but whether it’s a trivial toy or a game-changing tool depends largely on how we harness context. Context engineering is emerging as the not-so-secret ingredient that turns raw AI capability into practical, reliable solutions. Prompt engineering – the art of asking – was a great start, but context engineering – the art of informing – is what will elevate AI from neat demo to trusted associate in the legal field.

As lawyers and legal technologists, we don’t all need to become AI programmers. But we do need to become savvy about context. That means recognizing, in each use of AI, what information the model needs and ensuring it’s provided. It might mean working with our IT and knowledge management teams to integrate AI with our document repositories. It likely means rethinking how we draft, store, and annotate our documents knowing an AI may one day read them. And on an individual level, it means a shift in mindset: when an AI gives a poor answer, instead of concluding “this AI is dumb,” we should ask “what context was it missing?” – and then try again with better context.

For a profession built on precedent, evidence, and carefully crafted language, context engineering isn’t foreign – it’s second nature. We’ve always known the importance of the fine print and the full story. Now it’s about applying that same principle to working with AI. By doing so, we ensure that these powerful models truly serve us well: producing output that is accurate, relevant, and specific to our needs. In a way, context engineering aligns AI with the way lawyers have always worked – starting from the facts and law (context) and then applying reasoning to reach an answer.

As you move forward, experiment with context when you use generative AI. Be deliberate about what you feed into the model. You’ll likely find that a mediocre prompt with great context beats a clever prompt with no context, almost every time. And as the tools evolve, keep an eye on how they’re incorporating context behind the scenes. The best legal AI tools won’t ask you to master prompt kung-fu; they’ll quietly handle context for you, letting you focus on evaluating results and applying judgment.

The age of prompt engineering made us realize we could talk to our machines in plain language. The age of context engineering will ensure the conversation actually has substance. For legal professionals, that could mean the difference between AI outputs that waste billable hours and AI contributions that free you to do higher-value work. So let’s embrace context engineering – it’s time to feed our AIs a healthy diet of relevant information and watch them thrive.

Learn More: Further Reading on Context Engineering

For those interested in diving deeper into context engineering (in both legal and general AI settings), here are some excellent resources:

• “Context Engineering: Why Feeding AI the Right Context Matters” – Inspired Nonsense (Medium, Jan 2025). A concise article introducing the concept of context engineering, with examples of how structuring information can matter more than the choice of model, https://inspirednonsense.com/context-engineering-why-feeding-ai-the-right-context-matters-353e8f87d6d3.

• “Context Engineering: A Guide With Examples” – DataCamp (2023). An accessible tutorial on what context engineering is, common failures when context is missing, and how techniques like RAG and memory can make AI systems more robust. It clearly distinguishes context engineering from prompt engineering and gives practical tips on managing information flow, https://ai-pro.org/learn-ai/articles/why-context-engineering-is-redefining-how-we-build-ai-systems/.

• “Context Engineering vs. Prompt Engineering — What’s the Difference?” – Adnan Masood, PhD on Medium (Jun 2025). A detailed breakdown of how context engineering elevates AI for real-world applications. It emphasizes that prompt engineering is about the question, whereas context engineering is about providing the knowledge base for answers, and it enumerates key principles (dynamic context, full coverage, etc.) for successful AI deployment, https://medium.com/@adnanmasood/context-engineering-elevating-ai-strategy-from-prompt-crafting-to-enterprise-competence-b036d3f7f76f.

Each of these readings reinforces the central lesson: the future of working with AI isn’t about dreaming up clever prompts in isolation – it’s about engineering the context in which our questions are asked and answered. Happy reading, and happy context engineering!