Code preserves the what, not the why
AI makes code faster to write and change. The problem appears later: the repository contains the result, but it does not necessarily preserve the requirement, constraint or decision that produced it.
When another person or agent returns to that code, they have to reconstruct intent from names, comments and observed behavior. That reconstruction takes time and can reopen decisions the team already settled.
What gets lost when context is missing
Context is not a long explanation wrapped around the code. It is a set of concrete artifacts:
- the user problem that justified the change;
- requirements and acceptance criteria;
- rejected alternatives and the reasons they were rejected;
- technical and business constraints;
- evidence that the result satisfies the contract.
Without those links, onboarding takes longer, reviews repeat old debates, and agents work from an incomplete model of the system.
How PaellaDoc organizes the work
PaellaDoc treats context as part of the work rather than a separate document to maintain afterwards. It connects discovery, decisions, specifications, tasks, code and evidence so a team can trace the path between a need and its implementation.
The structure has three jobs:
- keep related artifacts under a shared identity;
- give each agent the rules and decisions relevant to its task;
- store verification beside the contract version that was checked.
Where the workflow changes
Product definition
Research, the decision and the requirement stay linked. A user story stops being an isolated summary and retains the evidence that produced it.
Technical design
Architecture decisions retain their constraints and alternatives. A team can review why a boundary exists before changing it.
Implementation and verification
The task reaches the agent with testable criteria. The result returns with logs, tests and other verification artifacts connected to the same contract.
What PaellaDoc does not replace
Structure does not make decisions for the team, turn a hypothesis into evidence or guarantee that a specification is correct. It makes decisions and proof visible so they can be reviewed without reconstructing them from scratch.
That is the goal: increasing implementation speed without reducing the ability to understand, review and change the product later. The principles of AI-first development describe the model in more detail.
Frequently asked questions
What is PaellaDoc?
PaellaDoc is a framework for preserving context across the whole development lifecycle, born from a simple pain: AI-generated code that becomes unreadable to its own author months later. Rather than documentation kept apart from the work, it captures the reasoning, requirements and decisions behind the code and keeps them connected to it, so the “why” survives long after the AI produced the “what”.
What is the context crisis in AI development?
It is the gap AI opens between how fast code is written and how fast its context disappears. The code flows quickly, but the reasoning behind it — the decisions, the constraints, the intent — is rarely captured. Months later that missing context makes your own code feel foreign, slows onboarding, and past decisions get re-litigated. The crisis is not code quality; it is lost knowledge.
Why does context get lost in AI-assisted development?
Because generation is instant and context capture is not. When an assistant writes a feature in minutes, the prompts, trade-offs and rejected options behind it usually go unrecorded. Traditional documentation lives in separate tools and rots. Without deliberately anchoring the reasoning to the code, each future change forces someone to reconstruct intent from scratch, which is exactly where the time savings quietly disappear.