KM-201b

Knowledge Capture

The practitioner course for knowledge extraction and documentation. Covers tacit knowledge extraction through structured interviews, decision capture methodology, process and workflow documentation patterns, the runbook format, passive capture pipelines, and AI-assisted capture. Includes quality gates for ensuring captured knowledge is accurate, usable, and maintained.

9 Lessons · ~0.5 Hours · 3 Modules

Instructor: ATLAS — Lead Instructor — Knowledge Management

Module 1: Tacit Knowledge Extraction

How to surface and capture what experts know but cannot easily articulate — through structured interviews, decision capture, and the discipline of extracting the 'why' not just the 'what'.

The Tacit Knowledge Problem: What Experts Know That They Don't Know They Know (4 min read)
Knowledge Interview Techniques: Extracting Expertise Through Structured Conversation (5 min read)
Decision Capture: Recording Not Just What Was Decided, But Why (4 min read)

Module 2: Process and Workflow Documentation

The difference between describing a process and capturing it, the runbook pattern, and passive capture strategies that extract knowledge from daily work without additional effort.

Process Mapping for Knowledge: Documenting vs. Capturing (4 min read)
The Runbook Pattern: Writing Procedures That Transfer Capability (5 min read)
Passive Capture Strategies: Meeting Notes, Decision Logs, and Slack-to-Wiki Pipelines (4 min read)

Module 3: AI-Assisted Knowledge Capture

How AI changes the economics of knowledge extraction, what can be captured automatically, and the quality gates that prevent AI capture from scaling knowledge debt instead of knowledge value.

Using AI for Knowledge Extraction: Interview Synthesis, Transcript Analysis, Pattern Recognition (4 min read)
Automated Capture Pipelines: What Can Be Captured Without Human Effort (4 min read)
Quality Gates for Captured Knowledge: Preventing Garbage-In, Garbage-Out (4 min read)