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- AI
Context Engineering
5 Courses
4 Hours
Skill IQ
This learning path introduces Context Engineering, the practice of designing, assembling, and optimizing context for large language models to improve relevance, accuracy, and efficiency. Learners will explore end-to-end information flows from indexing through retrieval, context assembly, compression, evaluation, safety, and orchestration. The path covers both foundational concepts and hands-on strategies, including context indexing, retrieval-augmented generation, compression techniques, pipeline orchestration with LangGraph, and monitoring with LangSmith, equipping learners to design scalable, reliable, and safe context-driven AI systems.
Content in this path
Context Engineering
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What You'll Learn
- You will learn how to design and implement context pipelines, including indexing, retrieval, and compression strategies.
- You will learn how to construct prompt templates and optimize context for relevance, coherence, and efficiency.
- You will learn how to evaluate context quality, model grounding, and safety for reliable AI outputs.
- You will learn how to orchestrate scalable, adaptive context workflows and monitor performance with observability tools.
Prerequisites
- Learners should have a basic understanding of large language models, embeddings, and retrieval-augmented generation, along with proficiency in Python and data handling. Familiarity with prompt engineering and AI pipelines is helpful but not required.
Related topics
- Artificial Intelligence
- Generative AI
- Large Language Models
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