Contextual Engineering Guide

LangChain AI Agents Using Contextual Engineering

Context engineering means creating the right setup for an AI before giving it a task. This setup includes:

• Instructions on how the AI should act, like being a helpful budget travel guide
• Access to useful info from databases, documents, or live sources.
• Remembering past conversations to avoid repeats or forgetting.
• Tools the AI can use, such as calculators or search features.
• Important details about you, like your preferences or location.

 *Context Engineering (From [LangChain](https://blog.langchain.com/context-engineering-for-agents/) and [12Factor](https://github.com/humanlayer/12-factor-agents/tree/main))*

[AI engineers are now shifting](https://diamantai.substack.com/p/why-ai-experts-are-moving-from-prompt) from prompt engineering to context engineering because…

context engineering focuses on providing AI with the right background and tools, making its answers smarter and more useful.

In this blog, we will explore how **LangChain** and **LangGraph** two powerful tools for building AI agents, RAG apps, and LLM apps can be used to implement **contextual engineering** effectively to improve our AI Agents.

This guide is created on top of [langgchain ai](https://github.com/FareedKhan-dev/contextual-engineering-guide) guide.

---

Table of Contents

• What is Context Engineering?
• Scratchpad with LangGraph
• Creating StateGraph
• Memory Writing in LangGraph
• Scratchpad Selection Approach
• Memory Selection Ability
• Advantage of LangGraph BigTool Calling
• [RAG with Contextual Engineering](#rag-with-contextual-en