Context Engineering for Developers

36mIntermediate2025-07-29

Authors

Deepak Goyal

Course details

This course introduces you to the emerging skill of Context Engineering in AI. Learn how to dynamically manage and deliver relevant data, tools, and workflows to large language models for accurate and reliable outcomes. Explore techniques like smart retrieval, summarization and context quarantine. Designed for AI practitioners and prompt engineers aiming to build better AI agents and RAG pipelines.

Learning objectives
Define context engineering and explain how it differs from prompt engineering.
Identify failure modes in AI systems due to poor context handling.
Design RAG-based pipelines for dynamic context retrieval.
Apply summarization and pruning techniques to manage large inputs.
Analyze real-world use cases where context engineering improves AI performance.

Skills covered

Generative AIArtificial Intelligence FoundationsArtificial Intelligence (AI)One-Off

Concepts

0. Introduction

01 - Introduction
02 - What you should know

1. Contenext Engineering Fundamentals

03 - Why context matters
04 - What is context engineering
05 - Systems thinking for context

2. Context Engineering in Practice

06 - Common context failure modes
07 - RAG and smart retrieval
08 - Context pruning and summarization
09 - Context quarantine
10 - Context offloading and long-term memory
11 - Apply context engineering
12 - Case studies and real-world applications

3. Moving Forward

13 - What's next