How to apply context engineering

LangChain · Beginner ·🤖 AI Agents & Automation ·9mo ago
Agents need context (e.g., instructions, external knowledge, tool feedback) to perform tasks. Context engineering is the art and science of filling the context window with just the right information at each step of an agent’s trajectory. There are many context engineering approaches, and Drew Breunig recently summarized six of the most common in his recent blog post titled "How To Fix Your Context." Here, we show how to apply each of these methods from scratch using LangGraph. Learn to build with LangGraph on LangChain Academy: https://academy.langchain.com/collections/quickstart/?utm_medium=social&utm_source=youtube&utm_campaign=q4-2025_youtube-academy-links_aw Repo: https://github.com/langchain-ai/how_to_fix_your_context Blog: https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.html Slides: https://docs.google.com/presentation/d/1UqJ9xB0QQuyQbILRxgsSyIgobtu3GpzoRrHrUwj7cCI/edit?usp=sharing
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from LangChain · LangChain · 0 of 60

← Previous Next →
1 Chat With Your Documents Using LangChain + JavaScript
Chat With Your Documents Using LangChain + JavaScript
LangChain
2 LangChain SQL Webinar
LangChain SQL Webinar
LangChain
3 LangChain "OpenAI functions" Webinar
LangChain "OpenAI functions" Webinar
LangChain
4 LangSmith Launch
LangSmith Launch
LangChain
5 LangChain x Pinecone: Supercharging Llama-2 with RAG
LangChain x Pinecone: Supercharging Llama-2 with RAG
LangChain
6 LangChain Expression Language
LangChain Expression Language
LangChain
7 Building LLM applications with LangChain with Lance
Building LLM applications with LangChain with Lance
LangChain
8 Benchmarking Question/Answering Over CSV Data
Benchmarking Question/Answering Over CSV Data
LangChain
9 LangChain "RAG Evaluation" Webinar
LangChain "RAG Evaluation" Webinar
LangChain
10 Fine-tuning in Your Voice Webinar
Fine-tuning in Your Voice Webinar
LangChain
11 Tabular Data Retrieval
Tabular Data Retrieval
LangChain
12 Building an LLM Application with Audio by AssemblyAI
Building an LLM Application with Audio by AssemblyAI
LangChain
13 Superagent Deepdive Webinar
Superagent Deepdive Webinar
LangChain
14 Lessons from Deploying LLMs with LangSmith
Lessons from Deploying LLMs with LangSmith
LangChain
15 Shortwave Assistant Deepdive Webinar
Shortwave Assistant Deepdive Webinar
LangChain
16 Cognitive Architectures for Language Agents
Cognitive Architectures for Language Agents
LangChain
17 Effectively Building with LLMs in the Browser with Jacob
Effectively Building with LLMs in the Browser with Jacob
LangChain
18 Data Privacy for LLMs
Data Privacy for LLMs
LangChain
19 "Theory of Mind" Webinar with Plastic Labs
"Theory of Mind" Webinar with Plastic Labs
LangChain
20 LangChain Templates
LangChain Templates
LangChain
21 Using Natural Language to Query Postgres with Jacob
Using Natural Language to Query Postgres with Jacob
LangChain
22 Building a Research Assistant from Scratch
Building a Research Assistant from Scratch
LangChain
23 Benchmarking RAG over LangChain Docs
Benchmarking RAG over LangChain Docs
LangChain
24 Skeleton-of-Thought: Building a New Template from Scratch
Skeleton-of-Thought: Building a New Template from Scratch
LangChain
25 Benchmarking Methods for Semi-Structured RAG
Benchmarking Methods for Semi-Structured RAG
LangChain
26 LangSmith Highlights: Getting Started
LangSmith Highlights: Getting Started
LangChain
27 LangSmith Highlights: Debugging
LangSmith Highlights: Debugging
LangChain
28 LangSmith Highlights: Datasets
LangSmith Highlights: Datasets
LangChain
29 LangSmith Highlights: Evaluation
LangSmith Highlights: Evaluation
LangChain
30 LangSmith Highlights: Human Annotation
LangSmith Highlights: Human Annotation
LangChain
31 LangSmith Highlights: Monitoring
LangSmith Highlights: Monitoring
LangChain
32 LangSmith Highlights: Hub
LangSmith Highlights: Hub
LangChain
33 SQL Research Assistant
SQL Research Assistant
LangChain
34 Getting Started with Multi-Modal LLMs
Getting Started with Multi-Modal LLMs
LangChain
35 Build a Full Stack RAG App With TypeScript
Build a Full Stack RAG App With TypeScript
LangChain
36 Auto-Prompt Builder (with Hosted LangServe)
Auto-Prompt Builder (with Hosted LangServe)
LangChain
37 LangChain v0.1.0 Launch: Introduction
LangChain v0.1.0 Launch: Introduction
LangChain
38 LangChain v0.1.0 Launch: Observability
LangChain v0.1.0 Launch: Observability
LangChain
39 LangChain v0.1.0 Launch: Integrations
LangChain v0.1.0 Launch: Integrations
LangChain
40 LangChain v0.1.0 Launch: Composability
LangChain v0.1.0 Launch: Composability
LangChain
41 LangChain v0.1.0 Launch: Streaming
LangChain v0.1.0 Launch: Streaming
LangChain
42 LangChain v0.1.0 Launch: Output Parsing
LangChain v0.1.0 Launch: Output Parsing
LangChain
43 LangChain v0.1.0 Launch: Retrieval
LangChain v0.1.0 Launch: Retrieval
LangChain
44 LangChain v0.1.0 Launch: Agents
LangChain v0.1.0 Launch: Agents
LangChain
45 Build and Deploy a RAG app with Pinecone Serverless
Build and Deploy a RAG app with Pinecone Serverless
LangChain
46 Hosted LangServe + LangChain Templates
Hosted LangServe + LangChain Templates
LangChain
47 LangGraph: Intro
LangGraph: Intro
LangChain
48 LangGraph: Agent Executor
LangGraph: Agent Executor
LangChain
49 LangGraph: Chat Agent Executor
LangGraph: Chat Agent Executor
LangChain
50 LangGraph: Human-in-the-Loop
LangGraph: Human-in-the-Loop
LangChain
51 LangGraph: Dynamically Returning a Tool Output Directly
LangGraph: Dynamically Returning a Tool Output Directly
LangChain
52 LangGraph: Respond in a Specific Format
LangGraph: Respond in a Specific Format
LangChain
53 LangGraph: Managing Agent Steps
LangGraph: Managing Agent Steps
LangChain
54 LangGraph: Force-Calling a Tool
LangGraph: Force-Calling a Tool
LangChain
55 LangGraph: Multi-Agent Workflows
LangGraph: Multi-Agent Workflows
LangChain
56 Streaming Events: Introducing a new `stream_events` method
Streaming Events: Introducing a new `stream_events` method
LangChain
57 Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve
Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve
LangChain
58 OpenGPTs
OpenGPTs
LangChain
59 Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
LangChain
60 LangGraph: Persistence
LangGraph: Persistence
LangChain

Related AI Lessons

AI agents are becoming the new IDE. Keep your hands on the wheel.
AI agents are transforming developer workflows, but teams must adapt to ensure quality and control
Dev.to · Jenuel Oras Ganawed
AI Agents Are Breaking One of the Most Important Rules of Software Design
AI agents are breaking a fundamental rule of software design, highlighting the need for new design principles
Dev.to · Cédric Pierre
Machine Limited Perception of Tasks
Learn about the limitations of machine perception in simulating human thoughts and attributes before a task, and why it matters for AI development
Medium · AI
AI Readiness for Engineering Teams: 15 Questions Before You Scale
Assess AI readiness for engineering teams with 15 practical questions to ensure governance and control before scaling
Dev.to AI
Up next
NEW Antigravity 2.0 + Agent OS is INSANE!
Julian Goldie SEO
Watch →