# Technical Document Extraction: Prompt Comparison Diagram This document provides a detailed extraction of the textual and structural information contained in the provided image, which compares two different Large Language Model (LLM) prompt structures for translation tasks. ## 1. Document Overview The image is a technical diagram illustrating the specific formatting used for two different models: **GPT-4** and **ALMA**. The image is divided into two horizontal sections separated by a dashed line. --- ## 2. Component Isolation ### Region 1: GPT-4 Prompt (Top Section) This section outlines a structured prompt format utilizing "System" and "User" roles, typical of chat-completion APIs. * **Header Label:** `GPT-4 Prompt` (Enclosed in a rectangular box) * **System Role Definition:** * **Label:** `System:` * **Content:** "You are a helpful translator and only output the result." * **User Role Definition:** * **Label:** `User:` * **Content Structure:** * `### Translate this from <source language> to <target language>, <source language>:` * `<source sentence>` * `### <target language>:` ### Region 2: Divider * **Visual Element:** A horizontal dashed line separates the GPT-4 section from the ALMA section. ### Region 3: ALMA Prompt (Bottom Section) This section outlines a more direct, instruction-based prompt format without explicit role-based headers. * **Header Label:** `ALMA Prompt` (Enclosed in a rectangular box) * **Content Structure:** * Line 1: `Translate this from <source language> to <target language>:` * Line 2: `<source language>: <source sentence>` * Line 3: `<target language>:` --- ## 3. Detailed Text Transcription | Element | Transcribed Text | | :--- | :--- | | **Top Header** | GPT-4 Prompt | | **GPT-4 System Instruction** | System: You are a helpful translator and only output the result. | | **GPT-4 User Instruction** | User: ### Translate this from \<source language\> to \<target language\>, \<source language\>: \<source sentence\> ### \<target language\>: | | **Bottom Header** | ALMA Prompt | | **ALMA Instruction** | Translate this from \<source language\> to \<target language\>: \<source language\>: \<source sentence\> \<target language\>: | --- ## 4. Structural Analysis and Comparison The diagram highlights the following technical differences between the two prompting strategies: 1. **Role Specification:** GPT-4 uses explicit `System:` and `User:` tags to define the context and the task. ALMA uses a flat text structure without role identifiers. 2. **Delimiters:** GPT-4 utilizes Markdown-style headers (`###`) to separate instructions from data fields. ALMA relies on line breaks and colons. 3. **Redundancy:** In the GPT-4 prompt, the `<source language>` is mentioned twice in the instruction line (e.g., "...to \<target language\>, \<source language\>:"). In the ALMA prompt, the instruction line is followed by a label for the source language on the next line. 4. **Completion Trigger:** Both prompts end with the `<target language>:` label, serving as a "stop sequence" or a trigger for the model to begin generating the translation.

# Technical Document Extraction: Translation Prompt Analysis ## Image Description The image contains two side-by-side text-based prompts for translation tasks, labeled "GPT-4 Prompt" and "ALMA Prompt". Both prompts use structured formatting with placeholders for language variables and source text. --- ### GPT-4 Prompt Section **Header**: `GPT-4 Prompt` (bold, centered) **System Instruction**: `System:` `You are a helpful translator and only output the result.` **User Instruction**: `User:` `### Translate this from <source language> to <target language>, <source language>: <source sentence> ### <target language>:` **Formatting Notes**: - Placeholders enclosed in angle brackets (`< >`) - Triple hash (`###`) used for section headers - Clear separation between system/user roles and instructions - Placeholder for `<source sentence>` indicates variable input --- ### ALMA Prompt Section **Header**: `ALMA Prompt` (bold, centered) **Instruction**: `Translate this from <source language> to <target language>: <source language>: <source sentence> <target language>:` **Formatting Notes**: - Simplified structure compared to GPT-4 - Direct translation instruction without role separation - Placeholders follow similar syntax (`< >`) - No explicit system/user role definitions --- ### Comparative Analysis | Feature | GPT-4 Prompt | ALMA Prompt | |------------------------|---------------------------------------|--------------------------------------| | Role Definition | Explicit system/user separation | No role separation | | Placeholder Density | 4 placeholders | 3 placeholders | | Instruction Complexity | Multi-step with formatting markers | Single-step direct instruction | | Visual Separation | Dashed line between prompts | No separation | --- ### Key Observations 1. **Placeholder Consistency**: Both prompts use identical placeholder syntax (`<source language>`, `<target language>`) 2. **Instructional Approach**: GPT-4 emphasizes role-based interaction while ALMA focuses on direct translation 3. **Formatting Differences**: GPT-4 uses markdown-style headers (`###`) while ALMA employs plain text 4. **Output Constraints**: GPT-4 explicitly states "only output the result" while ALMA implies this through structure --- ### Language Analysis - **Primary Language**: English (100% of content) - **Placeholder Syntax**: Angle bracket notation (`< >`) used consistently - **Special Characters**: - Triple hash (`###`) for section headers in GPT-4 - Angle brackets (`< >`) for variable placeholders - Dash (`-`) for separation in GPT-4 --- ### Structural Integrity All textual components have been extracted with spatial grounding: - GPT-4 section occupies top half - ALMA section occupies bottom half - Dashed line (`---`) serves as visual separator - Placeholders maintain consistent positioning across both prompts --- ### Conclusion The image presents two distinct translation prompt templates with different structural approaches. GPT-4 emphasizes role-based interaction with explicit formatting markers, while ALMA provides a minimalist direct instruction format. Both maintain consistent placeholder syntax for language variables.