Knowledge
Memory, documentation, context persistence, and institutional knowledge. Knowledge techniques solve the fundamental challenge of AI collaboration: the amnesia problem. They establish systems for retaining what matters across sessions, teams, and time.
8 methods| ID | Method | Description |
|---|---|---|
| FG-0701 | Structured History System 3 | Maintain a structured, versioned record of project history including plans, decisions, and health metrics. The history system serves as the institutional memory of the collaboration. It records what happened and why: the decisions made, the alternatives considered, and the lessons learned. When context is lost (as it inevitably is in AI collaboration), the history system provides recovery. |
| FG-0702 | Session Handoff Protocol | Define a structured format for transferring context between sessions. Every session end is a potential knowledge cliff: if context isn't captured, the next session starts from scratch. The Session Handoff Protocol creates a standardized 'shift change brief' that captures: what was accomplished, what's in progress, what decisions are pending, and what context the next session needs to continue seamlessly. |
| FG-0703 | Institutional Memory Repository | Create a persistent, organized repository of institutional knowledge that survives session resets, model changes, and team transitions. The Repository is the collaboration's long-term, external memory: accumulated knowledge that lives permanently, outside any single session's context window. It includes agent profiles, governance documents, operational procedures, and reference materials. Unlike session-specific context, the Repository's contents are always available and always current. |
| FG-0704 | Decision Records | Document significant decisions with their context, rationale, alternatives considered, and expected consequences. Decisions without documented rationale become mysterious over time: the team knows what was decided but not why, making it impossible to evaluate whether the decision is still appropriate. Decision Records preserve the thinking behind choices so they can be revisited intelligently. |
| FG-0705 | Journey Preservation 3 | Systematically capture and preserve war stories, breakthroughs, failures, and journey documentation. The collaboration's story: the messy, real, human story of building with AI: is itself valuable content. Journey Preservation ensures that significant moments are captured in real-time: the bug that led to a governance overhaul, the breakthrough that unlocked a new capability, the failure that taught the most important lesson. The journey is the content. |
| FG-0706 | Redundant Backup Strategy | Implement multiple, independent backup mechanisms for all critical data and knowledge. In human-AI collaboration, context loss is the ever-present threat. A single backup is not a backup: it's a single point of failure. Redundant Backup Strategy ensures that critical knowledge exists in at least three independent locations, using at least two different mechanisms, with regular verification that backups are current and restorable. |
| FG-0707 | Context Window Management 3 | Strategic management of what information occupies the finite context window available to AI agents during a session. The context window is the single most important technical constraint in AI collaboration. It determines what the agent knows, what it has forgotten, and what it will hallucinate to fill gaps. Effective context window management prioritizes high-value information, compacts or evicts stale context, and structures loading sequences to maximize signal-to-noise ratio within the available token budget. |
| FG-0708 | Certification-Driven Knowledge Extraction 3 | Using structured certification and training programs as knowledge extraction pipelines. As the human works through labs and modules, AI extracts training pairs, methodology notes, and technique documentation in real-time. The certification provides ground truth (labs validate answers), the AI provides extraction speed, and the corpus grows with every module completed. Each lab answer becomes a validated technique description. Each module becomes a structured knowledge domain. |