# Technical Document Extraction: LLM Epistemic Markers Diagram
## 1. Overview
This image is a flow diagram illustrating the difference in Large Language Model (LLM) outputs when using "Epistemic Markers" versus when they are absent. The diagram follows a top-down linear progression from a user query to a model processing stage, resulting in two comparative output formats.
## 2. Component Isolation
### Region 1: Header (Input Stage)
* **User Icon:** A black silhouette of a person's head and shoulders is positioned to the right of the input box.
* **Input Box:** A blue rounded rectangle containing white text.
* **Text Transcription:** "Which team won the 2022 NBA Finals?"
* **Flow Element:** A downward-pointing black arrow connects the Input Box to the Processing Stage.
### Region 2: Main Processing (LLM Stage)
* **Label:** The text "LLM" is positioned to the left of the central icon.
* **Icon:** A lime-green square with rounded corners containing a black circular emblem with a stylized "X" or interlocking knot design.
* **Flow Element:** A downward-pointing black arrow connects the LLM icon to the Output Stage.
### Region 3: Footer (Output Comparison Stage)
This region is contained within a large, light-blue rounded rectangle and is split into two parallel columns.
#### Column A: With Epistemic Markers (Left)
* **Sub-header Label:** A pale yellow rectangle containing the text: "With Epistemic Markers"
* **Output Box:** A light-blue box with a black border.
* **Transcribed Text:** "I think the Milwaukee Bucks won the 2022 NBA Finals, but I am not sure."
* **Visual Emphasis:** The words "**I think**" and "**but I am not sure**" are highlighted in **red text**, identifying them as the epistemic markers (indicators of uncertainty).
#### Column B: Without Epistemic Markers (Right)
* **Sub-header Label:** A pale yellow rectangle containing the text: "Without Epistemic Markers"
* **Output Box:** A light-blue box with a black border.
* **Transcribed Text:** "The Milwaukee Bucks won the 2022 NBA Finals."
* **Visual Emphasis:** All text is in standard black font, presenting the statement as a definitive fact without qualifiers.
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## 3. Technical Analysis & Logic Flow
The diagram demonstrates a specific behavior in AI communication:
1. **The Query:** The user asks a factual question about a past event.
2. **The Error:** In both output examples, the LLM provides incorrect information (The Golden State Warriors won the 2022 NBA Finals, not the Milwaukee Bucks).
3. **The Comparison:**
* **Epistemic Version:** By using markers like "I think" and "but I am not sure," the model signals its low confidence in the accuracy of the statement, potentially alerting the user to verify the fact.
* **Non-Epistemic Version:** The model states the incorrect information as an absolute truth, which constitutes a "hallucination" presented with high confidence.
## 4. Text Summary
| Element | Content |
| :--- | :--- |
| **User Query** | Which team won the 2022 NBA Finals? |
| **Processor** | LLM |
| **Output (With Markers)** | **I think** the Milwaukee Bucks won the 2022 NBA Finals, **but I am not sure.** |
| **Output (Without Markers)** | The Milwaukee Bucks won the 2022 NBA Finals. |
