Introduction to Context Engineering

Welcome to the official guide on Context Engineering. For the past several years, the dialogue around interacting with Large Language Models (LLMs) has been dominated by "prompt engineering." While crucial, this focus on the immediate instruction is only a small piece of a much larger puzzle. As we build more sophisticated, reliable, and autonomous AI systems, the focus must shift from the prompt itself to the entire ecosystem of information that surrounds it.

Context Engineering is the art and science of designing, managing, and optimizing this informational ecosystem. It is the discipline of architecting everything an AI model knows, remembers, and can do at the moment it generates a response. This guide will take you from the foundational principles to advanced implementation strategies, providing a comprehensive roadmap for building the next generation of intelligent applications.

The Paradigm Shift: From Prompting to Engineering

The move from prompt engineering to context engineering represents a fundamental shift in how we approach AI development:

  • From Instructions to Environments: Instead of just giving instructions, we are now building complete, data-rich environments for the AI to operate within.
  • From One-Shot to Continuous: We are moving beyond single, one-off questions to creating systems that maintain state, learn from interaction, and perform complex, multi-step tasks.
  • From Fragile to Robust: By grounding models in factual data and providing them with tools, we move from fragile, hallucination-prone systems to robust, reliable, and verifiable ones.

Thinking like a Context Engineer means you are no longer just a user of an LLM; you are the architect of its intelligence.

Core Concept: The Context Window

The context window is the finite amount of information (measured in tokens) that an LLM can "see" at any given moment. Everything—system instructions, user query, retrieved data, chat history—must fit into this window. It is the single most important constraint in LLM application development.

Why it Matters

The size of the context window dictates the complexity of tasks an LLM can handle. A small window can't hold enough information for a long conversation or a detailed document analysis. While windows are getting larger, they are not infinite, and larger windows come with higher costs and increased risk of the model "losing focus." Effective context engineering is the practice of using this limited space as efficiently as possible.

Core Concept: Retrieval-Augmented Generation (RAG)

RAG is the cornerstone of modern context engineering. It is the process of giving an LLM access to external, up-to-date, or proprietary information that it was not trained on. This is how you make an LLM an expert in your data.

The RAG Pipeline

  1. Indexing: Your external documents (e.g., PDFs, web pages, database records) are chunked into manageable pieces. Each chunk is then converted into a numerical vector by an embedding model and stored in a specialized vector database.
  2. Retrieval: When a user asks a question, their query is also converted into a vector. The system then searches the vector database to find the document chunks with the most similar vectors (i.e., the most semantically relevant information).
  3. Augmentation & Generation: The retrieved chunks of information are "augmented" by inserting them directly into the LLM's context window alongside the original user query. The LLM then generates an answer based on this newly provided context.

Core Concept: Agentic Architectures

An AI Agent is a system that goes beyond simple text generation. It can perceive its environment, reason about how to achieve a goal, and take actions using a set of tools. Context engineering is what enables this transformation from a language model to an autonomous agent.

Tool Use and ReAct

The most common agentic framework is ReAct (Reason + Act). The core idea is to engineer a context that encourages the LLM to cycle through a loop of thought, action, and observation.

A key part of the context is the tool definition. You describe the tools the agent can use, including their names, descriptions, and required arguments, often in a JSON format. When presented with a task, the LLM can "reason" that it needs a tool and "act" by outputting a specific JSON object to call that tool.

// Example Tool Call Output from LLM
{
  "tool_name": "get_stock_price",
  "arguments": {
    "ticker_symbol": "GOOGL"
  }
}

Core Concept: AI Memory

For an AI to have a coherent conversation or perform multi-step tasks, it needs memory. Context engineers design memory systems that manage and condense conversation history to fit within the context window.

Types of Memory

  • Conversational Buffer: The simplest form, where the last few turns of the conversation are kept in the context verbatim.
  • Summarization Buffer: As the conversation gets longer, an LLM is used to periodically summarize the history, saving token space.
  • Vector-Based Memory: Past interactions are stored in a vector database, allowing the agent to retrieve relevant memories in the same way RAG retrieves documents.

Core Concept: Structured I/O

For reliable agentic systems, we need the LLM's output to be predictable and machine-readable. We can't rely on parsing unstructured text. Context engineering solves this by providing strict output schemas (like JSON Schema or Pydantic models) directly in the context. This forces the LLM to generate perfectly formatted data that can be used by other parts of the application without fail.

Implementation: The Context Stack

Building a modern AI application involves choosing components for your "Context Stack":

  • Orchestration Framework: A library like LangChain, LlamaIndex, or Microsoft's Semantic Kernel that provides the tools to build and chain together the other components.
  • Language Model (LLM): The "brain" of the operation (e.g., GPT-4, Gemini, Llama 3).
  • Embedding Model: A specialized model used to turn text into vectors for semantic search.
  • Vector Database: A database optimized for storing and querying vectors (e.g., Pinecone, Chroma, Weaviate).

Implementation: Anatomy of a Modern Prompt

A "prompt" in a context-engineered system is not a single sentence. It's a complex template assembled from multiple components:


You are a helpful AI assistant. You must answer questions based ONLY on the provided context.



- Document 1: Context Engineering is the discipline of...
- Document 2: The RAG pipeline consists of...



Human: What is Context Engineering?
AI: It is the discipline of designing the informational ecosystem for an AI.



Based on the documents, what are the steps in the RAG pipeline?

Implementation: Evaluation

How do you know if your context engineering efforts are working? You must measure them. Evaluation in RAG and Agentic systems is a complex but critical field. Frameworks like RAGAs (Retrieval-Augmented Generation Assessment) help measure key metrics:

  • Faithfulness: How factually consistent is the answer with the provided context?
  • Answer Relevancy: How relevant is the answer to the user's query?
  • Context Precision & Recall: How relevant and comprehensive was the information retrieved by the RAG system?

Advanced: Advanced RAG Techniques

Basic RAG is powerful, but the state-of-the-art has evolved:

  • Hybrid Search: Combining traditional keyword search with semantic vector search to get the best of both worlds.
  • Re-ranking: Using a secondary, more powerful model to re-rank the initial set of retrieved documents for better relevance.
  • Query Transformation: Using an LLM to refine or expand a user's query before sending it to the retrieval system, or breaking a complex query into multiple sub-queries.

Advanced: Multi-Agent Systems

The next frontier is creating systems of multiple, specialized AI agents that collaborate to solve complex problems. For example, a "research team" might consist of a planning agent that breaks down a task, several worker agents that perform searches and analysis in parallel, and a final consolidation agent that synthesizes the results into a single report. The context for each agent must be carefully engineered to facilitate this collaboration.

Related: Vibe Coding

As context engineering tools and frameworks become more powerful, the human interaction style with AI evolves. Vibe Coding describes the fluid, conversational, and high-level development process where the human provides the "vibe" or strategic goal, and the AI handles the low-level implementation. It's a creative partnership enabled by the robust, context-aware systems we are now able to build.