---
schema: 1
type: term
title: inline citations
description: >-
  **Inline citations** — as used in the channel's captured material — are the
  numbered markers NotebookLM attaches to each answer, showing exactly which
  source passage generated that answer; clicking a citation jumps straight to
  the original quote [^0cc0dd07-dc88-5d83-8cf7-2f6e48821796@1032]. In the
  demonstrated workflow they serve two roles: the model returns guides and
  answers _with citations_ [^0cc0dd07-dc88-5d83-8cf7-2f6e48821796@2141], and you
  _click through_ those citations to read, verify, and save relevant passages
  onto the noteboard [^0cc0dd07-dc88-5d83-8cf7-2f6e48821796@2204].
canonical: >-
  https://liquidgold.cc/channel/everyinc/explore/term/inline-citations-ecec8483e2e7
markdown: >-
  https://liquidgold.cc/channel/everyinc/explore/term/inline-citations-ecec8483e2e7.md
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# inline citations

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/inline-citations-ecec8483e2e7

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

**Inline citations** — as used in the channel's captured material — are the numbered markers NotebookLM attaches to each answer, showing exactly which source passage generated that answer; clicking a citation jumps straight to the original quote [^0cc0dd07-dc88-5d83-8cf7-2f6e48821796@1032]. In the demonstrated workflow they serve two roles: the model returns guides and answers _with citations_ [^0cc0dd07-dc88-5d83-8cf7-2f6e48821796@2141], and you _click through_ those citations to read, verify, and save relevant passages onto the noteboard [^0cc0dd07-dc88-5d83-8cf7-2f6e48821796@2204].

In this channel's material, **inline citations** function as the trust mechanic of a sources-grounded research workflow. The concept appears in the NotebookLM walkthrough, where every answer the model produces carries numbered markers showing exactly which source passage generated it [^0cc0dd07-dc88-5d83-8cf7-2f6e48821796@1032]. Citations show up twice in the demonstrated workflow. First, they are what the model returns: Johnson's _reader guide_ prompt — context about himself plus a request for the most important sections to read — comes back as an interview-by-interview guide **with citations** [^0cc0dd07-dc88-5d83-8cf7-2f6e48821796@2141]. Second, they are what you act on: the _click-through_ step, where clicking a relevant passage's citation saves it onto the noteboard via _add to note_ [^0cc0dd07-dc88-5d83-8cf7-2f6e48821796@2204]. The practical takeaway for your own workflow: treat inline citations as your verification layer, not decoration. Instead of accepting an AI answer wholesale, use the citations as a reading scaffold — click through to the original passage, judge it yourself, and pin what resonates. That loop is what let the session move from 200,000 words of NASA oral-history transcripts to a curated set of notes and, eventually, an Apollo 1 documentary concept and opening script. To explore the technique in context, jump into the moments [^0cc0dd07-dc88-5d83-8cf7-2f6e48821796@52].