Imagine this: You hire the world’s smartest assistant. They know every language, every book, every spreadsheet formula. But when they show up, you give them no instructions, no tools, and no idea what task they’re supposed to perform.
The result? Confusion. Irrelevant answers. Wasted potential.
That’s what interacting with AI—especially large language models (LLMs)—feels like without context engineering.
As AI becomes more powerful and deeply integrated into our tools, the secret to getting high-quality, reliable results lies not in writing clever prompts, but in strategically designing what information the model sees, how it behaves, and what it knows.
This is the essence of context engineering.
What Is Context Engineering?
At its core, context engineering is about setting up the right environment for an AI system to perform at its best. Think of it like prepping the stage before a performance: lighting, sound, props—all set just right so the star (the AI) can shine.
In more technical terms, it’s about curating what goes into the model’s context window a temporary memory where all the relevant text lives during a task. The model only has access to what’s in that window when it’s responding.
Just like your computer’s RAM, the context window is limited (even if it's expanding over time). So what you put into it really matters.
For Dimensions of Context Engineering
1. Write Context-Forming New Memories
🧠Human Brain Analogy:
Imagine you just finished an insightful meeting. You jot down action items in your notebook and mentally store key decisions. Your brain is writing context — storing new information for future use.
đź’Ľ Real-life Example:
You're attending a training session. You write notes (scratchpad) during the session. Afterward, you add key insights to a knowledge base or personal journal (long-term memory).
đź’ˇ In AI:
Scratchpad: Short-term memory (within a session) — useful for immediate reasoning.
Long-term memory: Persistent storage across sessions — for cumulative learning.
2. Select Context-Recalling What’s Needed
🧠Human Brain Analogy:
You're writing a report, and you need to recall the customer feedback from last month. You don’t think about everything — just the relevant parts. Your brain selects the needed context.
đź’Ľ Real-life Example:
A chef preparing a dish doesn’t remember every recipe they’ve ever learned. They recall just the ingredients and steps for the current dish.
đź’ˇ In AI:
Tool selection: Like choosing a knife over a spoon.
Scratchpad recall: Bringing back temporary notes.
Memory recall: Querying long-term memories or knowledge bases.
3. Compress Context-Summarizing to Fit Limits
🧠Human Brain Analogy:
Before explaining a concept to a friend, your brain summarizes it. You leave out details that aren’t essential and focus on what’s most important.
đź’Ľ Real-life Example:
You’re preparing a presentation. The meeting is short, so you only include key metrics, trimming out redundant or less relevant slides.
đź’ˇ In AI:
Summarization: Condensing large text into core ideas.
Trimming: Removing noise or less useful information.
This helps fit everything within the model’s context window (like our mental bandwidth).
4. Isolate Context-Thinking in Mental Silos
🧠Human Brain Analogy:
You’re a parent, employee, and runner — but you compartmentalize these roles. While working, you block out distractions from home or training.
đź’Ľ Real-life Example:
A therapist listens to clients’ personal issues all day, but doesn’t mix one client’s case with another. They mentally separate and protect each session's context.
đź’ˇ In AI:
State partitioning: Each agent or task has its own "mental room."
Sandbox environments: Think of safe, isolated rooms where nothing leaks out.
Multi-agent context separation: Like multiple brains, each focused on their own task.
The Five Pillars of Context Engineering (With Real Examples)
1. Role and Behavior
What it means: Telling the model who it is and how it should act.
Why it matters: LLMs are generalists by default. But with the right instructions, they can take on any persona—an empathetic therapist, a blunt product manager, a senior financial advisor.
Example:
"You are a legal compliance expert. Answer concisely in a formal tone, citing specific GDPR clauses when possible."
Outcome: The AI adopts the right mindset, avoids fluff, and speaks in a voice that matches your expectations.
2. đź“š Knowledge Access
What it means: Supplying the AI with relevant, real-world information: documents, databases, policies, APIs, and more.
Why it matters: Pre-trained models don't know your business. If you don’t feed them context, they’ll guess—or hallucinate.
Example:
You upload your company’s leave policy and ask:
“Can you explain how casual leave differs from sick leave?”
Outcome: The AI extracts accurate, contextual answers from your document, not the internet.
3. 🧠Conversation Memory
What it means: Letting the AI remember what you've already talked about across sessions.
Why it matters: Nobody wants to repeat themselves. With memory, the AI feels more like a teammate who remembers past meetings.
Example:
Day 1: “Our product launch is August 15. Remind me a week before.”
Day 8: “Reminder: Your product launch is in 7 days.”
Outcome: Smart follow-up, personalized help, no backtracking.
4. 🛠️ Functional Tools
What it means: Connecting the AI to tools beyond just text—calculators, search engines, APIs, code runners.
Why it matters: LLMs can explain math, but they can’t calculate precisely unless paired with a tool. Likewise, they can’t fetch the latest news or run code unless integrated with a plugin.
Example:
“Using the calculator tool, what’s the interest on $50,000 at 7% over 5 years?”
Outcome: You get a precise number, not just a formula.
5. 👤 Personal Context
What it means: Letting the AI know about you: your background, goals, preferences, industry, or working style.
Why it matters: The more context you share, the more relevant and tailored the responses.
Example:
“I’m Sreeni here and non-technical founder in fintech. Explain zero-knowledge proofs like you’re pitching to an investor.”
Outcome: No jargon, no code—just a clean explanation that matches your level and needs.
But What About 10 Million Token Context Windows?
With models like Gemini and GPT-4.5 now processing hundreds of thousands to 1 million tokens, and rumors of 10M-token models on the horizon… you might wonder:
Will context engineering still matter?
Yes. More than ever. Here's why:
1. More Isn’t Always Better
Just dumping in more data doesn’t guarantee better answers. It often creates noise the model has to sift through. The signal gets lost.
2. Attention is Limited
Even in large context windows, models focus more on the most recent or most relevant information. Studies (including OpenAI's) show that attention to older tokens fades over time. Token #9,000,000 may never be seen.
3. Cost and Speed Tradeoffs
Longer contexts = more compute = higher costs + slower response times. You still want to be strategic about what goes in.
4. Precision Beats Volume
The smartest AI users know that carefully curated context will always beat a firehose of unfiltered data.
5. Curation Is a Human Skill
No matter how big the context window gets, someone still needs to decide what’s relevant—and how to structure it. That’s where context engineers shine.
Thanks
Sreeni Ramadorai
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