Context rather than fragments
Architecture, interfaces, tickets, terminology, data flows, and existing rules turn a plausible answer into a more suitable working basis.
AI & Development
AI-assisted development for existing systems
AI can accelerate analysis, implementation, testing, and documentation. In established TYPO3, Symfony, and Next.js/React systems, however, it only becomes useful through good context, architecture awareness, and accountable review.
Generating working code is fast. Delivering cleanly integrated solutions is something else.
Working approach
Why AI in established systems needs context
The model alone does not determine quality. What matters is which project information it may see, which rules apply, and who interprets its results within the established system.
Knowledge as a maintainable source
Documentation, Markdown, domain notes, or a verified CMS export are prepared so that content remains traceable, maintainable, and selectively discoverable.
Review and accountability
AI can make proposals and accelerate work. Architecture decisions, permissions, tests, and production changes remain with the responsible people and systems.
From knowledge to answer
Controlled context rather than a chat with full access
For an assistant, the factual source is defined first. Only then does a bounded answer context emerge, not from the browser DOM, private repository files, or arbitrary data.
1. Editorial source
Markdown can be the leading knowledge source. In CMS projects, an approved and versionable export takes this role.
2. LLM Wiki and OKF
The LLM Wiki idea is a readable, structured knowledge layer. Google Cloud’s Open Knowledge Format (OKF) makes content origin, language, and relationships verifiable.
3. Targeted retrieval
JSONL, SQLite FTS, or Solr are derived search layers. They select suitable excerpts instead of putting an entire body of content into every prompt.
4. Bounded answer
The model writes from selected evidence. The application determines sources, permissions, answer boundaries, and the next content shown to the visitor.
Markdown or a verified CMS export remains the traceable knowledge source. The index is a derived search aid.
Use in established systems
AI supplements systems instead of outsourcing accountability
In development
Analysis, planning, implementation, testing, documentation, and review benefit from reliable project context. That shortens paths but does not replace domain or technical judgment.
In digital products
A source-grounded assistant helps visitors and authenticated users understand content, functions, and useful next steps more easily.
The practical scenarios for websites and applications are on the AI Assistants page.
Boundaries belong in the concept
Rights, data, and tools are decided beforehand
A public assistant may explain published content only. In an application, context, roles, and permissions are determined server-side. Write actions need additional confirmation, authorization, and traceability.
Privacy, provider choice, logging, prompt-injection protection, and operating boundaries do not belong at the end of a project. They are part of the architecture.
From concept to application
What should AI explain, accelerate, or safeguard better in your existing system?
The starting point is a concrete work or visitor need, the available content, and the technical landscape. From there, a bounded and verifiable first step can be developed.