Context Engineering Micro-Credential

Context Engineering Micro-Credential

Duration: 3-4 Hours

Description

Prompt Engineering 101 teaches how to ask well. This session teaches how to make sure the model has the right information in front of it when you ask — the skill that matters most once you’re building agents instead of sending one-off prompts. Participants work with context windows as a limited, structured resource: what to include, what to leave out, how to summarize and compress history, and how to design memory so an agent stays useful across a long task instead of drifting or repeating itself.

Audience

This session is for developers and technical practitioners building agentic or multi-step AI applications who have completed Prompt Engineering 101 Micro-Credential (or have equivalent prompting experience) and are running into inconsistent results as tasks get longer or more complex.

Objectives

  • Distinguish context engineering from prompt engineering and explain why it matters for agents
  • Structure a context window deliberately: instructions, retrieved data, tool output, and history
  • Apply summarization and compression techniques to manage long-running tasks
  • Design short-term and long-term memory for an agent that stays coherent over time

Prerequisites

Completion of Prompt Engineering 101 Micro-Credential or equivalent prompting experience is expected. Basic Python familiarity is recommended.

Related AI Courses

See the full AI training roadmap and course directory for how this fits into a broader learning path.

Build the foundation first with Prompt Engineering 101 Micro-Credential, or for a full multi-day treatment, Prompt Engineering. This pairs naturally with Beginner’s Guide to Agentic AI Micro-Credential and Building Multi-Agent Systems Micro-Credential, where context management is the difference between an agent that works and one that drifts.

Course Outline

Module 1: The Context Window as a Resource

  • From Prompting to Context Engineering
  • What Goes In: Instructions, Retrieved Data, Tool Output, History
  • Why More Context Isn’t Always Better
  • Lab – Diagnose a Failing Agent by Inspecting Its Context

Module 2: Managing Context Over Time

  • Summarization and Compression Strategies
  • Short-Term vs. Long-Term Memory
  • Retrieval as a Context Management Tool
  • Lab – Add Rolling Summarization to a Multi-Turn Agent

Module 3: Designing for Long-Running Tasks

  • Preventing Context Drift and Repetition
  • Structuring Context for Multi-Agent Handoffs
  • Testing and Debugging Context-Related Failures
  • Lab – Redesign a Context Strategy for a Long-Horizon Task