All you need to know about Context Engineering

Key Concepts

Context Engineering, Prompt Engineering, RAG (Retrieval Augmented Generation), State and History, Memory (Short-term, Long-term), Vector Databases, Knowledge, AI Agents, Context Window, Context Compression, Context Isolation, MongoDB, Codebase Reader Agent, Review Agent, Requirements Preparation Agent, Implementation Steps Preparation Agent.

Context Engineering vs. Prompt Engineering

The video emphasizes the shift from "vibe coding" and basic prompt engineering to a more robust approach called context engineering. While prompt engineering is suitable for one-off tasks like content generation and format-specific output, context engineering is crucial for building high-quality, production-ready AI applications such as conversational AI, document analysis tools, and coding assistants.

• Prompt Engineering: One-off tasks, content generation, format-specific output.
• Context Engineering: Conversational AI, document analysis tools, coding assistants.

What is Context Engineering?

Context engineering involves providing relevant information to a Large Language Model (LLM) to enable it to complete tasks more effectively. This information includes:

• Retrieved Knowledge: Information retrieved from external sources.
• Tool Outputs: Results from using external tools.
• Prior Conversation: History of the conversation.
• User Input: The user's current request.

Context engineering encompasses:

• RAG (Retrieval Augmented Generation)
• State and History
• Memory (Short-term, Long-term)
• Prompt Engineering
• Structured Output

The Importance of Context Management

The video highlights that AI agents often engage in conversations spanning hundreds of turns, requiring careful context management strategies. This involves adding memory to AI agents, including short-term and long-term memory, and storing this data in vector databases for later retrieval.

Context Engineering Workflow

The context engineering workflow involves preparing the context before running the AI agent. This preparation includes:

1. Preparing the Context: Gathering relevant information based on the user's input or task.
2. Passing the Context to the AI Agent: Providing the prepared context to the AI agent.
3. Providing Memory and Knowledge During Agent Execution: Continuously supplying the agent with relevant memory and knowledge during its operation.

Four General Categories for Context Engineering

The video outlines four general categories for context engineering:

1. Write Context: Involves writing long-term memories, scratchpad notes (session state), and current state information.
2. Select Context: Focuses on retrieving only the relevant tools, scratchpad data, long-term memory, and knowledge needed for the task.
3. Compress Context: Aims to make the context fit within the context window by summarizing large codebases or trimming irrelevant tokens.
4. Isolate Context: Involves partitioning context based on state, holding an environment or sandbox, or partitioning across multiple agents.

Example: Automating Context Preparation with Multiple Agents

The video demonstrates a practical example using multiple agents to automate context preparation. These agents include:

• Codebase Reader Agent: Analyzes the codebase.
• Review Agent: Reviews the code.
• Requirements Preparation Agent: Prepares the requirements.
• Implementation Steps Preparation Agent: Prepares the implementation steps.

By providing a URL or a goal, these agents automatically generate a context document containing the necessary information to achieve the goal within the codebase. This context document serves as a detailed prompt that guides the AI agent.

Code Example: Preparing Context in Three Lines of Code

The video showcases a code snippet that prepares the context in just three lines of code. This involves providing the URL of a GitHub repository and the desired goal (e.g., "need to add authentication"). The agent then analyzes the repository and generates a context document outlining the steps required to achieve the goal.

Using MongoDB for Memory and Knowledge

The video demonstrates how to use MongoDB to store and retrieve memory and knowledge for AI agents. MongoDB can function as both a vector database and a key-value pair database. The steps include:

1. Setting up MongoDB: Creating a free account on MongoDB Atlas and obtaining the connection string.
2. Connecting to MongoDB: Using the connection string in the code to connect to the MongoDB database.
3. Storing Memory and Knowledge: Storing conversation history and relevant documentation in the database.
4. Retrieving Information: The AI agent retrieves relevant information from the memory and knowledge stores during execution to produce high-quality output.

Demonstration with Cursor and Win

The video demonstrates how context engineering can be applied in coding environments like Cursor and Win. By providing the generated context document to these tools, the AI can automatically implement features and fulfill requests based on the prepared context.

Conclusion

The video advocates for context engineering as a superior approach to prompt engineering for building production-ready AI applications. By providing AI agents with relevant context, memory, and knowledge, developers can significantly improve the quality and effectiveness of AI-powered tools. The video provides practical examples and code snippets to illustrate how to implement context engineering using tools like MongoDB and demonstrates its application in coding environments like Cursor and Win.