Learning Timeline
Key Insights
Input Quality Warning
RAG and Grounding do not fix poor or biased sources. If the uploaded documentation is flawed, the AI's 'grounded' answer will reflect those flaws.
Citation Benefits
Using NotebookLM forces the model to evaluate its own claims by linking them to specific parts of your uploaded documents, making verification significantly faster than standard LLM generation.
Prompts
Strict Context Constraint Prompt
Target:
General LLMs (ChatGPT, Claude, etc.)
Answer based only on this text. If you are unsure or the information is missing, say, "I don't know" instead of guessing.
Source Disagreement Analysis
Target:
Google NotebookLM
Identify where the provided sources disagree.
Coverage Gap Analysis
Target:
Google NotebookLM
What is missing from the coverage in these documents?
Alternative Perspectives Prompt
Target:
Google NotebookLM
What are alternatives to this topic with more real-world examples and use cases?
Step by Step
Performing Basic Grounding in LLMs
- Locate the specific transcript, PDF, documentation, article, or research paper required for context.
- Open your preferred Large Language Model (e.g., ChatGPT, Claude).
- Upload the file (if supported) or copy and paste the full text directly into the chat input field.
- Type the specific grounding prompt (see Prompt card) instructing the AI to use ONLY the provided text.
- Review the output to ensure the model has cited or referenced the provided text rather than external training data.
Executing RAG with Google NotebookLM
- Navigate to Google NotebookLM.
- Create a new notebook for your specific topic.
- Click the upload interface to add sources.
- Select and upload multiple source files (supports various file types like PDFs or Google Docs).
- Wait for NotebookLM to process and index the sources.
- Enter your query in the chat interface.
- Review the generated response, specifically checking the in-text citations provided by default to verify accuracy.
