Documentation on the fly (Continuous Documentation)

The concept of Continuous Documentation is at the intersection of experimentation and early industrial implementations. The idea of automatic dock generation is fraught with danger: if given complete freedom, AI will overwhelm the knowledge base with terabytes of auto-generated spam, exacerbating the GIGO problem. Best practices are now built around the concept of “Human-in-the-Loop” and filling based on AI knowledge deficits.

Historically, documentation has been the main bottleneck of knowledge management, and it’s boring to write documentation, it rarely pays a premium, and it’s the first thing engineers donate under deadlines, and as a result, knowledge bases become obsolete faster than the ink on virtual paper is drying.

With the development of generative AI, things are changing, and AI agents are able to take over the routine of collecting, synthesizing and writing documentation, making it a continuous, automatic process. Continuous Documentation (Continuous Documentation).

Approach #1: Documenting from Knowledge Deficit (Demand-Driven Context)

The traditional top-down approach to knowledge creation is that managers try to write all-in-one instructions in advance, creating overloaded, heavy structures that will never be useful half of.

The revolutionary alternative is methodology. Demand-Driven Context (DDC, On-Demand Context)proposed in the scientific work arXiv (2026).

DDC inverts the process of filling the knowledge base:

We run AI agents to perform real business tasks (write code, configure servers, respond to customers).
In the process of work, AI inevitably faces difficult situations where it lacks internal information (agent failure occurs).
This failure is recorded by the system and generates a point request to the person: “I did not have the specification of method X to perform task Y. Please describe this method.”
The human gives a short answer, which the AI automatically formats into a structured knowledge base article.

This approach, inspired by the practice of TDD (Test-Driven Development) in development, allows you to create the most compact, 100% demanded and accurate knowledge base, developing exactly where it is necessary for process automation.

Approach 2: Knowledge Extraction (Conversational Extraction)

A huge amount of engineering knowledge is born and dies in Slack, Microsoft Teams, Zoom calls and Jira/Linear ticket comments.

Modern practice of continuous documentation introduces background AI observers into work channels:

Scrapping chats: The AI bot analyzes the discussions, and when it sees a complex engineering dispute that ends with, "OK, let’s do this…" it automatically extracts the essence of the decision, the context, the alternatives, and it puts it in a technical paper or ADR.
Synthesis of calls (Transcripts-to-Docs): AI processes the audio recording of a technical meeting, removes the “water”, highlights key theses, agreements, responsible and automatically publishes the protocol of the meeting in the knowledge base with the tags of the participants.

The Rule of Success: Human-in-the-Loop

The biggest mistake in implementing AI documentation is the complete automation of publishing, and if the AI directly writes articles to the Wiki without moderation, the knowledge base will instantly become an unreadable stream of noise and hallucinations.

Right pattern: AI acts exclusively as a “Drafting Assistant”:

AI collects context, writes a draft of the article, tags metadata and links it to the repository/section.
The AI sends a push to the knowledge owner (Subject Matter Expert) with the request: "I documented the decision to integrate the database based on your yesterday’s Slack-trade. Please check and approve the draft."
The person makes a minute review, makes edits (if necessary) and presses the “Publish” button.

This ensures a high rate of filling the knowledge base while maintaining strict quality control of the content, protecting the system from degradation.

Useful materials and references

Demand-Driven Context: A Methodology for Building Enterprise Knowledge Bases Through Agent Failure - a scientific article on arXiv (2026), describing a revolutionary methodology for filling corporate knowledge bases based on the analysis of errors and information deficits in AI agents.
cADR (Continuous Architectural Decision Records) - an automatic tool from Yotpo, generating documentation of architectural decisions directly in the process of writing code based on Git-diffs.
Don’t Build That RAG Knowledge Base — Build a Living Knowledge Loop DEV Community article on the importance of building dynamic, self-renewing knowledge cycles instead of creating static and rapidly obsolete databases.
Continuous Knowledge Upkeep to Prevent RAG Decay - material from Brainfish on automation of processes of continuous updating and synchronization of knowledge bases with changes in the company’s products.