From Prompt Engineering to Context Engineering
The 777-1 Experiment
7 Projects. 7 Subagents. 7 Case Studies.
The Algorithm That Never Stops Learning.
Context Engineers
Case Studies
Code Reviews Analyzed
Refined Prompts
What is Context Engineering?
Traditional prompt engineering focuses on writing the perfect prompt upfront. But even well-written prompts have gaps. Context engineering recognizes this reality.
Instead of overloading a single prompt with every requirement, context engineering uses specialized subagents to provide domain-specific context just-in-time, in a specific order that matters.
Lazy Prompt
Gaps & Missing Context
7 Subagents
Sequential Review
Sophisticated Result
Production-Ready
Meet the 7 Context Engineers
Each subagent specializes in detecting specific issues that prompts miss
Amber
Responsive Design
Kristy
Functionality
Micaela
UI Consistency
Lindsay
Accessibility
Eesha
State Management
Daniella
Code Quality
Cassandra
Integration
The 777-1 Journey
The Problem
After reviewing 129 AI-generated applications, the same issues appeared again and again. AI needs better context, not just better prompts.
The Experiment
7 diverse projects, 7 specialized subagents, systematic testing. Each transformation documented via Git commits for full transparency.
The Result
A predictive algorithm that keeps improving. Patterns extracted to predict prompt failures before execution.
Featured Case Study
Each case study documents the complete transformation of an application through all seven Context Engineers. Watch as a simple prompt evolves into production-ready code.
Projects In Progress
Subagent Reviews
Documented
Try It Yourself
Want to see what our Context Engineers think of your prompt? Get instant feedback from all 7 subagents.
Ready to Explore Context Engineering?
Dive into the experiment, meet the team, and transform how you work with AI