Understanding Context in Artificial Intelligence

We’ve spent the last year obsessing over prompt engineering. And for good reason — how we ask the question impacts the output we get from GenAI. But here’s the truth most people haven’t realized yet: Prompt engineering is just the tip of the iceberg. In the not-so-distant future, you won’t even see prompts anymore. Most of them will be buried in buttons, voice assistants, and agents running behind the scenes. What will matter is what’s underneath: Context. If you’ve ever wondered why GenAI sometimes gives great answers and other times sounds like it’s hallucinating or winging it — this is why. Context engineering is the discipline of giving GenAI the right information, in the right format, at the right time. It’s how we shape the environment the AI operates in — and in a knowledge industry like law, that environment is everything. When we provide proper context — the facts of a case, the jurisdiction, the client history, the structure of a document — we get far better answers. When we don’t? We get fluff. Or worse — confident nonsense. This isn’t theoretical. It’s playing out right now in every GenAI tool lawyers use — from Harvey to Newcode.ai, from Legora to Microsoft 365 Copilot. Most of us have been so focused on writing better prompts that we’ve missed what actually powers reliable legal GenAI: 🧠 Organizing our documents so they can be referenced as context 🧠 Structuring notes and transcripts so AI can find key insights 🧠 Using summaries and metadata so systems know what matters 🧠 Cleaning our data and curating knowledge so responses are grounded in our facts In my latest article, I break down what context engineering is (in plain English), how it works in legal use cases, and what you can do today — whether you're a lawyer, legal tech vendor, or innovation leader. We explore real-world examples, discuss how today’s tools are already leveraging context behind the scenes, and offer a roadmap for law firms and corporate legal teams to start engineering their context now — to get better results and reduce risk. 🚨 If you’ve tried GenAI and thought “this isn’t useful,” chances are the model wasn’t the problem. The context was. Have you seen context make or break your AI experience? Are you preparing your data to be AI-ready? I’d love to hear how your team is thinking about this. #legaltech #artificialintelligence #aiforsmartpeople #innovation

Stop blaming ChatGPT, Claude , or Grok for bad outputs when you're using it wrong. Here's the brutal truth: 90% of people fail at AI because they confuse prompt engineering with context engineering. They're different skills. And mixing them up kills your results. The confusion is real: People write perfect prompts but get terrible outputs. Then blame the AI. Plot twist: Your prompt was fine. Your context was garbage. Here's the breakdown: PROMPT ENGINEERING = The Ask CONTEXT ENGINEERING = The Setup Simple example: ❌ Bad Context + Good Prompt: "Write a professional email to increase our Q4 sales by 15% targeting enterprise clients with personalized messaging and clear CTAs." AI gives generic corporate fluff because it has zero context about your business. ✅ Good Context + Good Prompt: "You're our sales director. We're a SaaS company selling project management tools. Our Q4 goal is 15% growth. Our main competitors are Monday.com and Asana. Our ideal clients are 50-500 employee companies struggling with team coordination. Previous successful emails mentioned time-saving benefits and included customer success metrics. Now write a professional email to increase our Q4 sales by 15% targeting enterprise clients with personalized messaging and clear CTAs." Same prompt. Different universe of output quality. Why people get this wrong: They treat AI like Google search. Fire off questions. Expect magic. But AI isn't a search engine. It's a conversation partner that needs background. The pattern:  • Set context ONCE at conversation start • Engineer prompts for each specific task  • Build on previous context throughout the chat Context Engineering mistakes:  • Starting fresh every conversation  • No industry/role background provided  • Missing company/project details • Zero examples of desired output Prompt Engineering mistakes:  • Vague requests: "Make this better" • No format specifications  • Missing success criteria • No tone/style guidance The game-changer: Master both. Context sets the stage. Prompts direct the performance. Quick test: If you're explaining your business/situation in every single prompt, you're doing context engineering wrong. If your outputs feel generic despite detailed requests, you're doing prompt engineering wrong. Bottom line: Stop blaming the AI. Start mastering the inputs. Great context + great prompts = consistently great outputs. The AI was never the problem. Your approach was. #AI #PromptEngineering #ContextEngineering #ChatGPT #Claude #Productivity #AIStrategy Which one have you been missing? Context or prompts? Share your biggest AI struggle below.

Context Engineering and Prompt Engineering aren’t the same thing. As soon as your use case moves beyond single turn prompts, the difference really starts to matter. 𝐇𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐜𝐨𝐫𝐞 𝐢𝐝𝐞𝐚: Context = Prompt + Memory + Retrieval + Tool Specs + Execution Traces. Prompting = Asking a good question. Context Engineering = Setting the whole stage. 𝐋𝐞𝐭’𝐬 𝐛𝐫𝐞𝐚𝐤 𝐢𝐭 𝐝𝐨𝐰𝐧: 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: - Best for summaries, rewrites, Q&A - Works well for static, one shot tasks - Focuses on templates, tone, and instruction clarity - Breaks down as task complexity increases 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: - Powers agents, multi-step workflows, and tool use - Involves memory, retrieval, orchestration - Built for dynamic systems that evolve mid-task - Most failures come from context sprawl or leakage 𝐊𝐞𝐲 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 𝐢𝐧 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞: 👉 Primary Goal: Prompt: Write clearer instructions Context: Manage what the model knows and remembers 👉 Use Case Fit: Prompt: Simple interactions Context: Multi-turn workflows, real systems 👉 Memory: Prompt: Stateless or minimal Context: Structured, persistent, scoped 👉 Scalability: Prompt: Limited beyond basic tasks Context: Built for complex reasoning at scale 👉 Failure Mode: Prompt: Misunderstood instructions Context: Too much, too little, or irrelevant data 👉 The Takeaway: Prompting helps a model respond. Context engineering helps a model reason. If you’re building copilots, agents, or decision-making systems Context is where scale, reliability, and intelligence start to emerge. Let me know if you want to see how this distinction plays out in real architectures. Follow for more insightful content. #AI #PromptEngineering #LLM #AIagents #Copilots #AIsystems #Productivity #WorkflowAutomation #ArtificialIntelligence

Everyone’s suddenly talking about 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. Here’s why it matters. In the AI gold rush, most people focus on the LLMs. But in reality, context is the product. Context engineering is the emerging discipline of designing, assembling, and optimizing what you feed a LLM. It’s the art and science behind how RAG, agents, copilots, and AI apps actually deliver business value. It includes: - What information to surface (data selection, chunking, and formatting) - How to frame the user intent (prompt design, agent memory, instructions) - How to dynamically adapt to each interaction (tool use, grounding, policies) Think of it as the new software architecture but for AI reasoning. And just like traditional engineering disciplines, it’s becoming repeatable, measurable, and mission-critical. 💡The future isn’t just “prompt engineering.” It’s context engineering at scale; where the AI is only as good as the ecosystem of inputs it’s wired into.

I've often observed that as AI model context windows grow, the challenge of pinpointing specific information intensifies, the classic 'needle-in-a-haystack' problem. However, recent advancements are reshaping this landscape. The 'needle-in-a-haystack' benchmark is now a crucial tool for assessing long-context AI performance. Excitingly, GPT-4.1 models are setting new standards. With a 1 million token context window (10 typical novels or 8+ react codebases), they're not just processing more data, but doing so with exceptional accuracy. What's particularly impressive is their ability to consistently retrieve hidden information, the 'needle,' regardless of its position within that vast context. This reliability is critical for applications demanding precise information retrieval, such as legal document analysis, complex coding tasks, and customer support interactions. This breakthrough signifies a major step forward in long-context understanding, and I'm eager to see the innovative applications it will enable. What are your thoughts on the implications of these advancements? #AI #LargeLanguageModels #DeepLearning #ContextWindow #Innovation

In working with AI, I’m stopping before typing anything into the box to ask myself a question : what do I expect from the AI? 2x2 to the rescue! Which box am I in? On one axis, how much context I provide : not very much to quite a bit. On the other, whether I should watch the AI or let it run. If I provide very little information & let the system run : ‘research Forward Deployed Engineer trends,’ I get throwaway results: broad overviews without relevant detail. Running the same project with a series of short questions produces an iterative conversation that succeeds - an Exploration. “Which companies have implemented Forward Deployed Engineers (FDEs)? What are the typical backgrounds of FDEs? Which types of contract structures & businesses lend themselves to this work?” When I have a very low tolerance for mistakes, I provide extensive context & work iteratively with the AI. For blog posts or financial analysis, I share everything (current drafts, previous writings, detailed requirements) then proceed sentence by sentence. Letting an agent run freely requires defining everything upfront. I rarely succeed here because the upfront work demands tremendous clarity - exact goals, comprehensive information, & detailed task lists with validation criteria - an outline. These prompts end up looking like the product requirements documents I wrote as a product manager. The answer to ‘what do I expect?’ will get easier as AI systems access more of my information & improve at selecting relevant data. As I get better at articulating what I actually want, the collaboration improves. I aim to move many more of my questions out of the top left bucket - how I was trained with Google search - into the other three quadrants. I also expect this habit will help me work with people better.

The shift from "prompt engineering" to "context engineering" represents a fundamental evolution in how we build AI systems, moving away from "LLMs-in-a-loop" towards the concept of a true "LLM OS". But to effectively engineer context and design agent memory, you need to understand the anatomy of a context window. => Breaking it down: the anatomy of a context window An agent's context window shouldn't just be an in-memory Python list accumulating chat messages, it should be a carefully engineered system with distinct components: 📋 𝗦𝘆𝘀𝘁𝗲𝗺 𝗣𝗿𝗼𝗺𝗽𝘁𝘀: Define the agent's core behavior and control flow, like an OS kernel defining how the system operates 💾 𝗠𝗲𝗺𝗼𝗿𝘆 𝗕𝗹𝗼𝗰𝗸𝘀: Persistent units of context that evolve over time, enabling agents to learn from interactions and maintain long-term state 📁 𝗙𝗶𝗹𝗲𝘀 & 𝗔𝗿𝘁𝗶𝗳𝗮𝗰𝘁𝘀: Working data that agents can access, manipulate, and iterate on, from PDFs to executable source code 💬 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝗕𝘂𝗳𝗳𝗲𝗿: The conversation stream where actual work happens (user messages, responses, and tool interactions) 🔧 𝗧𝗼𝗼𝗹 𝗦𝗰𝗵𝗲𝗺𝗮𝘀: Specifications for APIs that enable agents to take actions, from modifying their own memory (MemGPT-style) to calling external services => Context engineering & memory management Think of frameworks like Letta as an "LLM OS": managing context windows the way operating systems manage hardware resources. Just like traditional computing, context is divided into layers: * 𝗞𝗲𝗿𝗻𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁: Framework-managed state (memory blocks, files, system prompts) that persists and can be modified through APIs * 𝗨𝘀𝗲𝗿 𝗖𝗼𝗻𝘁𝗲𝘅𝘁: The active conversation layer that pulls in external data (via MCP) and interacts with kernel context through tools By thoughtfully designing your memory blocks, files, tools, and their interactions, you're essentially architecting the "brain" that your agent runs on. The framework handles the plumbing behind movement of blocks of tokens, while you (the agent developer) design how information flows, persists, and evolves over time. See our full blog post for more on understanding context windows and how to engineer them - we also dive a little deeper into the OS analogy.

★ 𝗔𝗗𝗩𝗔𝗡𝗖𝗘𝗗 𝗔𝗜 𝗜𝗦 𝗔 𝗧𝗛𝗢𝗨𝗚𝗛𝗧 𝗣𝗔𝗥𝗧𝗡𝗘𝗥, 𝗡𝗢𝗧 𝗔 𝗖𝗛𝗔𝗧𝗕𝗢𝗧 ★ OpenAI's latest model, o3, again surpasses all prior benchmarks in reasoning, math, and coding. But are you really using these high-powered models to their full potential? Most AI users are stuck in the "ask-and-answer" trap, treating advanced AI like a souped-up search engine or a typical back-and-forth with ChatGPT. That's a fundamental misunderstanding. ➤ 𝗦𝗧𝗢𝗣 𝗔𝗦𝗞𝗜𝗡𝗚 𝗤𝗨𝗘𝗦𝗧𝗜𝗢𝗡𝗦, 𝗦𝗧𝗔𝗥𝗧 𝗦𝗛𝗔𝗥𝗜𝗡𝗚 𝗣𝗥𝗢𝗕𝗟𝗘𝗠 𝗦𝗣𝗔𝗖𝗘𝗦 Advanced reasoning models aren't meant to give us faster chat responses. They're meant to change how we think and expand our own cognitive capabilities. Models like o1 / o3, Thinking Claude, and the latest Gemini experiments can handle complex and nuanced 𝗠𝗘𝗚𝗔𝗣𝗥𝗢𝗠𝗣𝗧𝗦 that are thousands of words long. Give them: ↳ Entire Mental Models: A complete framework for thinking about a specific domain. ↳ Ontologies & Structured Knowledge: Detailed instructions that shape the model's understanding and approach. ↳ Textbooks, even: Massive amounts of information to ground the model in a particular field. Then tell it to address your needs from there. These models give us a superhuman-level capability to: ↳ Deconstruct Complexity: Break down messy problems into core components. ↳ Navigate Uncertainty: Reason through ambiguity and incomplete information. ↳ Generate & Evaluate: Create new frameworks, strategies, and even code, then critically assess them. Here's how to turn advanced AI into a powerful extension of your intellect: 𝗕𝗨𝗜𝗟𝗗 𝗬𝗢𝗨𝗥 𝗢𝗪𝗡 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 𝗕𝗟𝗨𝗘𝗣𝗥𝗜𝗡𝗧 》》𝐼𝑁𝑆𝑇𝐸𝐴𝐷 𝑂𝐹: Treating interactions & your knowledge as isolated. 》》》》𝐶𝑂𝑁𝑆𝐼𝐷𝐸𝑅 𝑇𝐻𝐼𝑆: Develop a Personal Context Blueprint - a living document outlining your goals, constraints, resources, and mental models. Use it as a foundation for your interactions with the AI.       𝗣𝗥𝗢𝗕𝗘 𝗙𝗢𝗥 𝗟𝗘𝗩𝗘𝗥𝗔𝗚𝗘 𝗣𝗢𝗜𝗡𝗧𝗦 》》𝐼𝑁𝑆𝑇𝐸𝐴𝐷 𝑂𝐹: Using direct Q&A format. 》》》》𝐶𝑂𝑁𝑆𝐼𝐷𝐸𝑅 𝑇𝐻𝐼𝑆: Focus on identifying high-leverage points within your problem space. Example: "Based on the provided Contextual Blueprint, identify three areas where a small change could have an outsized impact on my desired outcome of [xyz]." 𝗖𝗢𝗚𝗡𝗜𝗧𝗜𝗩𝗘 𝗟𝗢𝗔𝗗 𝗔𝗥𝗕𝗜𝗧𝗥𝗔𝗚𝗘 》》𝐼𝑁𝑆𝑇𝐸𝐴𝐷 𝑂𝐹: Using AI for everything (or nothing) 》》》》𝐼𝑀𝑃𝐿𝐸𝑀𝐸𝑁𝑇: Strategically offload high-cognitive-load, low-impact tasks to the AI (e.g., data processing, initial research, generating variations). Reserve your own cognitive bandwidth for high-impact, strategic decisions, and judgment calls. ➤ 𝗧𝗛𝗘 𝗥𝗘𝗔𝗟 𝗖𝗛𝗔𝗟𝗟𝗘𝗡𝗚𝗘 We're underutilizing the most powerful tools of our time. Stop thinking of advanced AI as a chatbot, and start thinking with it as a thinking partner. This shift is the key to unlocking the true potential of advanced reasoning models (and our own potential too). #AI

The AI skills gap isn't about prompting anymore that's dead ☠️ —it's about context engineering. Two years ago, everyone was talking about "prompt engineering" as the hot new skill. Companies were hiring "prompt engineers" for six-figure salaries. The hype was real. But here's what changed: almost everyone became a prompt engineer. I did a post yesterday on just how easy this is. Writing clever prompts became table stakes. Here's where your focus should be → Context engineering. Let's break down the difference 👇 → Prompt engineering = crafting the right question for a single interaction → Context engineering = architecting the AI's entire information environment Think of it this way: If prompt engineering is writing a brilliant instruction, context engineering is deciding what happens before and after that instruction—what's remembered, what's pulled from tools or memory, and how the whole conversation is framed. This is why I feel n8n has started pulling people away from Clay because you have the ability to give an Agent more Context. This is key because: ✅ Memory & Continuity: Real applications need AI that remembers previous interactions, not blank-slate responses ✅ Accuracy: Feeding relevant documents and data reduces hallucinations dramatically ✅ Scale: A clever prompt might work in demos but fail with diverse users—context engineering builds robust systems ✅ Competitive Advantage: Anyone can tweak prompts, but architecting intelligent information flow? That's the real skill. Here's the thing, we've moved from optimizing sentences to optimizing knowledge. Anyone can build an Agent with a Prompt that just talks but most will fail at context engineering, if you can master it your Agent will truly understand you.

Why “Context Engineering” Is the Real Core of LLM Applications The term prompt engineering often brings to mind simple, one-off instructions—clever phrases we toss at LLMs in casual use. But that framing falls short in real-world, production-grade AI systems. In modern LLM applications, the real differentiator is context engineering—a delicate balance of art and science in crafting the full environment the model operates within. It’s a science Context engineering involves: - Crafting task instructions and explanations - Supplying few-shot examples - Injecting relevant knowledge through RAG - Incorporating multimodal inputs, tool outputs, user state, and history - Compacting and organizing content to fit within token constraints Too little or irrelevant context, and performance suffers. Too much or poorly structured context, and token costs spike—or the model gets confused. Doing this right is non-trivial. It’s a technical discipline. It’s also an art: Great context engineers develop an intuitive sense of how LLMs “think.” They understand what will guide the model’s reasoning—and what will distract, overwhelm, or mislead it. It’s less about manipulation, more about orchestrating cognition. And it's just one layer: A full-fledged LLM app needs far more than great context: - Breaking problems into modular control flows - Packing and sequencing context across steps - Routing calls to the right model (based on cost, latency, or capability) - Building generation-verification loops and UI/UX patterns - Managing evals, prefetching, guardrails, observability, security... Context engineering is foundational—but not the whole stack. #agenticai #LLM #contextengineering #promptengineering