Master the Retrieval-Augmented Generation (RAG) pipeline for personal data using open-source models, local vector stores, and lightweight tools to achieve “style cloning” without costly fine-tuning.

In this 45-minute hands-on session, you’ll build a self-sovereign AI agent that writes like you - powered by a privacy-first, open-source framework.

We start with Data Sovereignty: learn how to legally export your data (e.g., X/Twitter or LinkedIn archives) and convert JSON or CSV files into a clean “voice corpus.”

Next, explore Voice Vectorization via RAG - embedding your content into a private vector store so your AI “remembers” your style and topics without retraining.

Finally, Deploy the LLM (e.g., Gemma 3) locally and apply prompt engineering to generate authentic, on-topic content: tweets, posts, or emails, in your own voice.

Leave with portable code to run the agent entirely offline: $0 API costs, full privacy, and total control of your digital persona.