All you need to know about Context Engineering

# Stop Wipe Coding, Start Context Engineering: Build High-Quality AI Applications with MongoDB 👉 Try MongoDB 8.0 here: https://mdb.link/mervin-praison-mdb 👉 Sign up for a free cluster here: https://mdb.link/mervin-praison-cluster Read more MongoDB blogs! MongoDB on Medium: https://medium.com/@MongoDB MongoDB on Dev Community: https://dev.to/mongodb Follow MongoDB Socials! YouTube: https://www.youtube.com/channel/UCK_m2976Yvbx-TyDLw7n1WA LinkedIn: linkedin.com/company/mongodbinc Instagram: instagram.com/mongodb/?hl=en X: twitter.com/MongoDB https://blog.langchain.com/context-engineering-for-agents/ code: https://mer.vin/2025/07/context-agent/ https://docs.praison.ai/ https://github.com/MervinPraison/PraisonAI/ Inspired by Cole Medin and Rasmus In this video, I dive deep into why context engineering is revolutionising AI development - even Andrej Karpathy prefers it over traditional prompt engineering! While vibe coding might feel good, without proper context, it leads to errors and suboptimal results. Let me show you how to build production-ready AI applications using context engineering. ## What is Context Engineering? Context engineering is the practice of providing relevant information to large language models (LLMs) to help them complete tasks more effectively. This includes: - Retrieved knowledge - Tool outputs - Prior conversation history - User inputs - RAG (Retrieval-Augmented Generation) - State and history management - Memory systems - Prompt engineering - Structured outputs As Cognition Labs states: "Context engineering is effectively the number one job of engineers building AI agents." Timestamp: 0:00 - Introduction to Context Engineering 0:23 - What is Context Engineering 0:40 - Context Engineering Components 1:08 - Memory and Knowledge in AI Agents 2:23 - Context Engineering Categories 3:47 - Multi-Agent Context Preparation 5:05 - Setting Up the Environment 5:30 - Running Context Preparation Agent 6:44 - Using Context in Cursor/Windsurf 7:17 - MongoDB for Memory and Knowledge 8:16 - Implementation Agent Demo 9:37 - MongoDB Canvas Overview 10:07 - Conclusion ## Why Context Engineering Matters Anthropic emphasises that agents often engage in conversations spanning hundreds of turns, requiring careful context management strategies. This involves: - Adding short-term and long-term memory to AI agents - Storing data in vector databases - Retrieving information when required - Providing documentation and knowledge to AI agents - Managing tool outputs effectively ## The Context Engineering Framework ### 1. **Write Context** - Long-term memories are stored in scratch pads - Session state management - Runtime data retrieval for agents ### 2. **Select Context** - Retrieve only relevant tools - Pull from scratchpad/session data - Access long-term memory - Retrieve relevant knowledge ### 3. **Compress Context** - Fit data into context windows - Summarise large codebases - Retain relevant tokens - Remove irrelevant information ### 4. **Isolate Context** - Partition context by state - Create sandboxed environments - Distribute across multi-agent systems ## Implementation Demo In this tutorial, I demonstrate a powerful multi-agent system that includes: - Codebase reader agent - Review agent - Requirements preparation agent - Implementation steps preparation agent With just 3 lines of code, you can create the context required for AI agents! ## MongoDB Integration I show you how to use MongoDB as both: - A vector database for embeddings - A key-value pair database for structured data MongoDB enables customizable memory and knowledge storage for your AI applications, making it perfect for production-ready systems. ## Key Differences **Prompt Engineering:** - One-off tasks - Content generation - Format-specific outputs **Context Engineering:** - Conversational AI - Document analysis - Tool integration - Coding assistants ## Getting Started The video includes a complete walkthrough of: - Setting up your environment - Configuring API keys - Installing dependencies - Running the context preparation agent - Implementing features with the prepared context - Storing and retrieving from MongoDB ## Results Watch as the system: - Analyzes GitHub repositories - Prepares implementation blueprints - Generates detailed documentation - Provides step-by-step implementation guides - Works seamlessly with tools like Cursor and Windsurf #ContextEngineering #AIAgents #MongoDB #LLM #MachineLearning #ArtificialIntelligence #Programming #TechTutorial Avoid vibe coding and use context engineering for robust AI applications. Supplying relevant data and knowledge to **llm** models improves accuracy and reliability! Context engineering is more effective than prompt engineering for **ai engineering** and ensures better **ai agent development**.