AI Context Engineering: The Step That Shapes AI Output
- Patrick Law
- Aug 11
- 2 min read
Most AI errors don’t happen during generation — they happen before you even type your prompt. That’s where AI context engineering comes in. At Singularity, it’s the step that shapes how AI understands the task, aligns with our workflow, and delivers output in the exact format we expect.
What is AI Context Engineering?
AI context engineering is the process of feeding AI the right foundation before it starts generating. In Singularity’s workflow, this foundation includes:
AI Background — Verified, relevant knowledge sources such as manuals, SOPs, and datasets.
AI Preferences — Our style rules, tone of voice, and formatting requirements.
AI Examples — Sample outputs showing exactly what “good” looks like.
This isn’t about making AI “smarter.” It’s about giving it the information, constraints, and examples it needs to align with our process from the start.
How Singularity Uses It
Our workflow always starts with a clean slate:
Create a New AI Project Folder — Even if the task is familiar, every project gets a fresh workspace.
Load Background Sources — Only verified, high-trust documents go into context.
Define Preferences — We include formatting, tone, and output structure expectations.
Add Examples — We show the AI sample outputs so it understands what the final product should look like.
By the time we write the first prompt, the AI is already aligned with our standards.
Why It Matters in Singularity’s Workflow
We still see AI hallucinations — no one eliminates them entirely. But context engineering lets us guide the AI toward:
The right format for our deliverables.
The tone our audience expects.
The level of accuracy and structure we need to maintain consistency.
It’s not just about getting an answer — it’s about getting the right kind of answer.
Recap: Context vs. Prompt Engineering
Quick recap —
Prompt engineering: Asking the question better.
Context engineering: Making sure AI already knows the facts, style, and examples before it answers.
One improves the question. The other shapes the answer.
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