Building a Personal Intelligence Layer for AI Context

The Personal Intelligence Layer

Building a personal intelligence layer involves creating a structured, portable artifact that captures professional insights, frameworks, and metrics across your career. By maintaining this repository, you provide LLMs with a persistent memory of your specific expertise, preventing the cold-start problem where AI assistants lack context about your past wins, losses, and methodologies.

Implementation Strategy

• Data Ingestion: Use a raw folder to drop unstructured data, including YouTube transcripts, meeting notes, PDFs, and documents. The system processes these files to extract key insights while stripping out company-confidential information.
• Classification and Routing: The build script categorizes extracted information into specific domains (e.g., Paid Advertising, SEO, Leadership) and associates them with the relevant company or project context.
• Artifact Generation: The system outputs an index.html file, which the author prefers over Markdown for its visual structure. This file serves as the primary knowledge base that can be uploaded to Google Drive or stored locally to be referenced by AI models like ChatGPT or Claude.
• Automation: The build process tracks processed files via a processed.md log to prevent duplicate ingestion. It can optionally integrate with MCP (Model Context Protocol) servers to pull data directly from Gmail, Google Drive, or Slack.

Workflow

1. Setup: Run the build script to configure your name, role, and preferred knowledge categories.
2. Staging: Move new notes or documents into the raw folder.
3. Processing: Execute the build command to extract insights, classify them, and review the proposed updates before committing them to the index.html intelligence layer.
4. Integration: Point your AI assistant to the folder containing your intelligence layer to ground its responses in your prior experience.