A New Paradigm for AI
The performance of Large Language Models (LLMs) is not just about model size. It's fundamentally determined by the information they receive. This application explores "Context Engineering," the formal discipline of designing, managing, and optimizing this information to build truly intelligent systems. It marks a shift from the art of "prompting" to the science of information architecture for AI.
Prompt Engineering
The traditional approach. It treats context as a single, static string of text, manually crafted to get a desired output. It's powerful but often brittle, hard to scale, and more of an art form.
Context Engineering
The systematic approach. It defines context as a dynamic, structured assembly of components like instructions, knowledge, memory, and tools. It's a scalable, modular engineering discipline.
The Building Blocks of Context
Context Engineering is built on three foundational pillars that manage the lifecycle of information. Click on each pillar to explore the key techniques that bring it to life. This section explains the core components that are assembled to create sophisticated AI systems.
① Retrieval & Generation
Sourcing the raw materials of context, from generating reasoning steps to fetching external knowledge.
② Processing
Transforming and refining information to make it more effective and efficient for the model to use.
③ Management
Organizing, compressing, and storing context to overcome memory limits and ensure consistency.
Intelligent Systems in Action
When the building blocks are combined, they form powerful, agentic systems capable of complex tasks. This section demonstrates how an LLM evolves from a simple text generator into a proactive agent that can reason, act, and collaborate.
Tool-Integrated Reasoning: The ReAct Framework
A core pattern for agentic behavior is the "Reason + Act" (ReAct) loop. The LLM doesn't just answer; it thinks, decides on an action (like using a tool), observes the result, and then thinks again. This allows it to solve problems that require interacting with the outside world. Click "Animate" to see the cycle in action.
🤔 Thought
"I need to find the capital of France. I should use the search tool."
⚡ Action
search("capital of France")
👀 Observation
Tool returned: "Paris"
The Frontier: Evaluation & Challenges
While powerful, today's AI agents still face significant hurdles. This section explores how we measure their performance and highlights the fundamental research gap that defines the next wave of AI innovation.
Agent Performance on Web Tasks
The WebArena benchmark tests an agent's ability to complete real-world tasks on websites. The results show a clear gap between current state-of-the-art systems and human-level performance, highlighting the difficulty of real-world interaction.
The Core Challenge: Comprehension vs. Generation
The survey identifies a critical asymmetry: LLMs are becoming masters of understanding complex inputs, but struggle to generate equally complex, coherent, long-form outputs. They can read the book, but they can't write one yet.
Comprehension ✅
📚
Excels at processing vast, complex information.
Generation ⚠️
📝
Struggles with long-range planning and coherence.
Closing this gap is the key to unlocking the next level of AI capability, requiring new architectures focused on planning and world modeling.