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.
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