Overview of Block Types
When 12scribe generates Smart Notes, the AI does not produce a single wall of text. Instead, it creates structured content blocks — distinct units of information, each formatted in the way that best communicates its content. A definition gets an emphasis block. A comparison gets a table. A step-by-step argument gets a logic chain.
There are six content block types available. The AI selects the appropriate type automatically based on the nature of the information it extracts from your transcript. You can also change a block's type after generation if you prefer a different format. The six types are: text, quote, table, logic chain, emphasis, and list. Each is explained in detail below.
The variety of block types is what makes Smart Notes more useful than a plain transcript summary. Different information structures require different visual formats to be easily understood. A list of items should look like a list, not a paragraph. A comparison should look like a table, not a run-on sentence. Content blocks ensure the right format for the right content.
Text Blocks
Text blocks are the most common type. They contain summarized paragraphs that capture the main points of a discussion in clear, readable prose. When a speaker explains a concept, describes a process, or provides context, the AI condenses that explanation into a concise text block.
Text blocks differ from the raw transcript in important ways. The transcript captures every word spoken, including filler words, repetitions, false starts, and digressions. A text block distills the meaning — what the speaker communicated, expressed efficiently. A two-minute spoken explanation might become a three-sentence text block that conveys the same information in a fraction of the reading time.
The detalization level affects text block length and density. At Low detalization, text blocks are brief summaries. At High, they include more supporting detail, examples, and nuance from the original discussion.
Quote Blocks
Quote blocks preserve the speaker's exact or near-exact words. The AI creates quote blocks when the specific phrasing matters — a memorable statement, a precise definition given by the speaker, a controversial claim, or a passage where paraphrasing would lose meaning.
Quote blocks appear with distinct visual styling that sets them apart from AI-generated summaries. This distinction is important: when you see a quote block, you know those are the speaker's words, not the AI's interpretation. This makes quote blocks particularly valuable for:
- Academic lectures where exact definitions matter
- Meetings where someone made a specific commitment ("I will deliver the report by Thursday")
- Interviews where the interviewee's own words carry weight
- Any context where attribution and precision are important
Each quote block links to the timestamp where the words were spoken, so you can tap to verify the quote against the original audio.
Table Blocks
Table blocks organize information into rows and columns. The AI generates tables when the content involves comparisons, properties, categories, or any structured data that benefits from a grid layout.
Common scenarios that produce table blocks include:
- A professor comparing two or more theories, listing their characteristics side by side
- A meeting participant listing project phases with their timelines and owners
- A speaker describing multiple items with shared attributes (features, costs, ratings)
- Classification systems with categories and examples
Tables in Smart Notes are scrollable and well-formatted. Each cell contains concise text, and the table headers clearly label what each column represents. For lectures that involve heavy comparison or classification, table blocks become one of the most valuable output types — they organize scattered verbal comparisons into a format you can study at a glance.
Logic Chain Blocks
Logic chain blocks represent step-by-step reasoning, cause-and-effect sequences, or argument progressions. They display as connected steps, showing how one idea leads to the next in a logical flow.
The AI creates logic chains when it detects sequential reasoning in the transcript: "First... then... therefore..." or "Because X, we see Y, which leads to Z." Logic chains are common in:
- Scientific explanations where phenomena follow causal sequences
- Mathematical or philosophical proofs with sequential steps
- Business arguments where premises lead to conclusions
- Historical narratives where events cause subsequent events
Visually, logic chain blocks show each step as a distinct element with connectors indicating the flow from one step to the next. This format makes complex reasoning easy to follow and review, especially when the speaker's verbal delivery was nonlinear or included digressions between steps. The AI reconstructs the clean logical flow from the messy spoken version. Learn how logic chains fit into broader lecture analysis structures.
Emphasis Blocks
Emphasis blocks highlight critical information that deserves immediate attention. The AI uses this block type for key definitions, important warnings, fundamental principles, or any content that represents a core concept the listener should not miss.
Emphasis blocks appear with distinct visual formatting — typically a highlighted background or border that draws the eye. They are intentionally short (usually one to three sentences) and convey a single important idea with maximum clarity.
Common triggers for emphasis blocks include:
- "The key thing to remember is..."
- Formal definitions of terms or concepts
- Stated requirements or prerequisites
- Critical warnings or exceptions to rules
Because emphasis blocks are visually prominent, having too many of them dilutes their impact. The AI is selective about what it flags with emphasis — at Medium detalization, you might see two or three emphasis blocks in a typical lecture, each marking a genuinely important concept.
List Blocks
List blocks present enumerated items, steps in a process, or collections of related points. They appear as clean bulleted or numbered lists, extracted from moments where the speaker explicitly listed multiple items.
The AI generates list blocks when it detects enumeration patterns in the transcript: "There are four main reasons..." or "The steps are: first... second... third..." Lists are also created when the speaker mentions multiple related items across several sentences without explicitly numbering them — the AI recognizes the underlying list structure and formats it accordingly.
List blocks are distinct from logic chain blocks in that they do not imply sequential causation. A list of reasons a market crashed is a list (parallel items). The sequence of events that led to the crash is a logic chain (sequential causation). The AI distinguishes between these patterns to give you the appropriate format.
Together, these six block types give Smart Notes the structural variety needed to represent any kind of spoken content. The AI's ability to select the right format automatically is what transforms a raw transcript into organized, study-ready notes.