Day 3: Build your own AI Assistant

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Day 3: build your custom knowledge assistant

This is where the Operator Sprint pays off most directly: you build an AI expert on YOUR knowledge base that cites sources and doesn’t make things up

RAG stands for Retrieval Augmented Generation and it sounds complex but the concept is simple: before answering your question, the system searches your documents for relevant information and includes that in the context

This grounds responses in your actual data rather than the model’s training, which dramatically reduces hallucination and enables domain-specific expertise

NotebookLM for zero-code RAG

Google’s NotebookLM requires no setup and works remarkably well

Upload PDFs, Google Docs, YouTube videos, or websites and the system becomes an expert on that content with inline citations

Audio Overviews generate podcast-style discussions of your documents, Mind Maps visualize complex topics, Deep Research in the Plus tier provides comprehensive analysis across your sources

This is the fastest path to a working knowledge assistant... under an hour from nothing to a functional system

Claude Projects as an alternative

Upload documents to a Claude Project and every conversation in that project references them automatically

More flexible than NotebookLM when you need to create outputs like documents and code rather than just query information

The insight most people miss: one focused project per task beats one massive project with everything, a project for “client proposals” with relevant case studies and pricing works better than a general “work stuff” project with hundreds of files competing for attention

You can also create knowledge containers in Claude Skills... invest time working with Skills, it’s worth it

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Understanding what’s happening under the hood

For those who want to go deeper: documents get split into chunks and converted to numerical representations called embeddings, those embeddings get stored in a vector database, when you ask a question your query becomes an embedding and the database finds the most similar document chunks, those chunks plus your question go to the LLM which produces a grounded answer

You don’t need to build this yourself... NotebookLM and Claude Projects handle it, but understanding the mechanism helps you troubleshoot when results aren’t what you expect

Today’s assignment: build a NotebookLM notebook with documents from your actual work... client files, research papers, internal docs, whatever you reference repeatedly, then build a parallel Claude Project with the same content, compare the outputs, notice how grounded responses feel completely different from generic AI answers

Stay curious, talk to you tomorrow.

CoolDeep AI
Helping you stay ahead with AI & productivity