Context Vigilance: A Neurotic model of Human + AI Product Development
This is a practical playbook for building real products with AI as a co-developer. It is explained with a real example of building a real product with AI, and it is battle tested. We have the battle scars to show. We hope you stay safe.
ACE It: a playbook and toolkit for AI-Augmented Product Development Workflow
Introduction
In prototyping a web application that would use AI, we were swept up into the Vibe Coding craze that kicked off in late 2024. To put things in context, we -- The Lossless Group -- began using Code Generators to rapidly create a Front-End to Augment-It. We did, it took about 2 weeks.
But to iterate on our quick success, we quickly found ourselves in the AI Dregs and it became a massive misadventure. In software product development, What is AI Good For?
Just to create a name, our emergent, battle-tested process we shall call Accelerated Context Engineering (ACE).
This is a comprehensive guide designed to seamlessly integrate AI-powered tools into development processes for teams of all sizes.
ACE provides a modular, scalable approach to incorporating artificial intelligence into software development workflows, from small startup teams to large enterprise organizations.
Key Philosophy: Working with AI is fundamentally similar to working with a development team the old fashioned way - it requires
- upfront orientation
- continuous alignment
- clear, modular documentation, continuously updated
- well-defined interfaces
- iterative collaboration
The Need for Rigorous Product Work
Experienced software developers (who are not problem-market domain experts) can only perform at peak-performance, deliver well-developed products, and work together efficiently.... with a lot of upfront and continuous work before, around, with, during, and after the software engineering phase.
Anyone who has worked with highly-paid, well recommended software engineers knows that regardless of talent, skill, and motivation, developing new software products does not always go well. They often, even more often than not, take longer, come out wrong, need huge refactors, cause organizational delays, and can lead a good number of people to rage quit.
More experienced people have learned the hard way that there is a huge amount of upfront and necessary design, systems architecture, product management artifacts along with various forms of documentation and project workflows. With this kind of product work, building a new software product has a much higher chance of success.
Still, since Facebook introduced Hacker Culture, most every rapid-growth company was founded by software engineers, led and scaled by software engineers, often who were young and did not have the hazing of working at large organizations that could afford to have many non-engineers spending lots of time preparing and planning. So, depending on you, the readers, experience.... you may have gained your professional experience in the "Move fast and break things" culture of technology innovation of the past 20 years where documentation was something engineers reluctantly made after they had "shipped it" and others had to figure out how to work with what was shipped.
Our experience cooperating with code generative AI suggests:
- AI Copilots need an extremely rigorous set of documentation that function is iterative, living, and highly useful tools in getting to workflows that produce real code and not Spaghetti Code. The alternative is to make every prompt a roll of the dice, and to end up in some kind of vibe coding purgatory.
- AI Copilots are actually VERY GOOD and VERY FAST, with extraordinary consistency, at developing, maintaining, updating, and formatting the very same documentation they need.
Current State of AI:
Today’s AI coding models are quick and useful, but initial enthusiasm will meet a harsh reality:
LLMs predict text rather than understand entire systems:
AI Models:
- make enormous and wild assumptions, frequently and repeatedly
- improvise redundant code with no organic attempt to seek or use established patterns, guidelines, data models, or naming conventions.
- do not learn or remember anything outside the Context Window
- actively ignore or miss cross-file links to components, utility functions, classes, preferring to write all of the requirements into a single file.
- sometimes invent APIs or versions,
- often introduce new and unwanted frameworks, libraries, or other shortcuts,
- default to installing the most-well known libraries, often unwanted
- do not execute, profile, or secure code,
- retrieval may be stale;
Privacy, licensing, latency, and cost remain practical constraints.
The ACE toolkit and method has codified coping with those realities in mind — the techniques and architecture described here were created to avoid the limits of code generation with LLMs,.
If understood and followed deliberately, we believe you will have our experience: we can Vibe and ACE our way into reliable, testable, repeatable, and auditable workflows for teams of any size.
What Awaits You
This documentation is a practical playbook for building real products with AI as a co-developer. It is organized into five parts:
- Foundation & Philosophy — how to think about AI-augmented development, work iteratively, and write prompts that actually guide results.
- Models Under the Hood & Tooling — what modern models can (and can’t) do; modalities; tokens and context windows; “Thinking” vs. standard decoding; function/tool calling.
- Complex Features Engineering — designing the data layer, integrating APIs, assembling UIs with shared components, and composing apps with microfrontends.
- Safety & Security — baseline security and data-handling, lightweight LLM-assisted audits, and using tests as executable specifications (LLM TDD).
- Examples & Reference Implementation — a real-world, end-to-end example that puts every principle into practice (microfrontends, module federation, shared UI, AI-powered workflows) with specs and apps you can adapt.
- Practical Use Cases Ideas — example concepts you can build on top of our system
Bonuses:
- A complete, working example — the Augment-It monorepo — demonstrates every principle in action through a real data augmentation platform with Microfrontends, Microservices, AI Integrations, and Scalable Architecture.
- Our ACE Documentation Library is publicly accessible, sorted into Specifications, Blueprints, Reminders, and Prompts. These are all on our website at https://lossless.group/vibe-with/us and our GitHub public content repository at https://github.com/lossless-group/lossless-content
Table of Contents
1. Foundation & Philosophy
2. Models Under the Hood & Tooling
3. Complex Features Engineering
4. Safety & Security
5. Examples & Reference Implementation
Complete System Architecture
Core Applications (Microfrontends)
Shared Infrastructure
Implementation Artifacts
6. Other Use Case Ideas
This project represents a comprehensive approach to AI-augmented development, designed to scale with your team's needs while maintaining code quality and developer experience.