Context Engineering

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.

The five components

A context engineering system has five components. Each folder implements one against the same running example (a Spring PetClinic codebase with ADRs):

#	Component	What it does	Folder
1	Corpus	The organizational material that defines how you think, build, and decide	`01-corpus/`
2	Retrieval	Identifies which portions of the corpus are relevant to a given request	`02-retrieval/`
3	Injection	Gets retrieved context into the model's working memory at decision time	`03-injection/`
4	Output	Produces reviewable artifacts (code, PRs, docs) shaped by that context	`04-output/`
5	Enforcement	Ensures the generated output actually reflects the retrieved context	`05-enforcement/`

Plus comparisons/ — the same task run with and without context engineering, plus how CE differs from RAG, Copilot, and agent frameworks.

A system with only components 1–3 is a RAG system. The output and enforcement layers are what make CE different — they make the generated content reviewable and governable.

Running the examples

All examples use Amazon Bedrock with Claude. Each folder has its own requirements.txt and README.md with a runnable command.

Prerequisites:

Python 3.11+
AWS account with credentials configured (aws configure or env vars)
AWS region that supports Claude and Titan (e.g. us-east-1, us-west-2)

This repo uses Anthropic Claude for generation and Amazon Titan for embeddings. Titan and most Bedrock foundation models are auto-enabled on first invocation — no action needed.

Anthropic Claude requires a one-time First Time Use (FTU) form per AWS account. If your account has never used Anthropic models on Bedrock, the first script run will fail with AccessDeniedException. To fix:

Open any Anthropic Claude model in the Bedrock model catalog
Fill the First Time Use form (company, use case — about a minute)
Submit — access is granted immediately, no review queue

If you're in an AWS Organization child account, the form must be submitted from the management account to inherit access.

Quickstart:

git clone https://github.com/outcomeops/context-engineering.git
cd context-engineering/01-corpus
pip install -r requirements.txt
python ingest_adrs.py ./sample-adrs

Set the model via environment variable if you want to override the default:

export BEDROCK_MODEL_ID="us.anthropic.claude-sonnet-4-5-20250929-v1:0"
export AWS_REGION="us-east-1"

Why this repo exists

Most AI coding assistants produce generic output. An engineer using a generic assistant still has to adapt the output to local patterns — the assistant doesn't know what your team decided last quarter, what your compliance framework requires, or why you picked one library over another.

A context-engineered system produces output that already conforms to local patterns, because the retrieval layer has fed the model the relevant ADRs, code, and standards at decision time. The enforcement layer ensures the output actually cites what it relied on.

This repo exists to show the pattern in code, end-to-end, so teams can build it themselves or evaluate commercial tools that claim to do it.