A working reference implementation of context engineering
I've been presenting at local meetups about Context Engineering, RAG, Skills, etc.. I even have a vbrownbag coming up on LinkedIn about this topic so I figured I would make a basic example that uses bedrock so I can use it in my talks or vbrownbags. Hopefully it's useful.
A working reference implementation of context engineering launched on April 17, 2026 with 46 votes, #893 of 21,248 launches that month and more than 95% of that year's launches.
In plain words
This is a working reference implementation of context engineering built with AWS Bedrock, designed for educational and demonstration purposes. It provides a practical example of context engineering, retrieval-augmented generation (RAG), and skills integration that developers and technical presenters can use in talks and training sessions. The implementation serves as a teaching tool for understanding how to structure and apply contextual information in AI applications.
written from the facts on this page · September 2026
From the sources
Contribute to outcomeops/context-engineering development by creating an account on GitHub.
A working reference implementation of context engineering — the discipline of designing, retrieving, and injecting the information an AI system needs to produce accurate, organization-specific outputs. This repo is the code companion to What Is Context Engineering? on outcomeops.ai. The glossary defines the concepts; this repo shows them running end-to-end against a real corpus on Amazon Bedrock. Context engineering treats context as a first-class engineering artifact — version-controlled, retrievable, and enforceable — rather than as prompts typed into a chat window. A context engineering system has five components. Each folder implements one against the same running example (a Spring…from github.com
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