## Text Extraction: Prompt Engineering Examples ### Overview The image presents examples of prompt engineering for a system designed to generate questions from given answers and identify if an answer is noncommittal. It includes a core template, an answer relevancy prompt, and several input-output examples. ### Components/Axes The image is structured into three main sections: 1. **Core Template:** Defines the instruction for the system to return output in JSON format, adhering to a specified JSON schema. It emphasizes the use of double quotes and proper escaping. 2. **Answer Relevancy Prompt:** Explains how to generate a question from a given answer and identify if the answer is noncommittal. It defines noncommittal answers as evasive, vague, or ambiguous, assigning a value of 1, while substantive answers are assigned a value of 0. Examples of noncommittal answers are provided. 3. **Examples:** Provides input-output pairs demonstrating the system's functionality. Each input consists of a "response," and the corresponding output includes a "question" and a "noncommittal" value (0 or 1). ### Detailed Analysis or ### Content Details **Core Template:** * `{instruction}`: Placeholder for instructions. * "Please return the output in a JSON format that complies with the following schema as specified in JSON Schema: {output\_schema}Do not use single quotes in your response but double quotes, properly escaped with a backslash." * `{examples}`: Placeholder for examples. * "Now perform the same with the following input" * `input: {input_json}` * `Output:` **Answer Relevancy Prompt:** * "Generate a question for the given answer and identify if the answer is noncommittal." * "Give noncommittal as 1 if the answer is noncommittal (evasive, vague, or ambiguous) and 0 if the answer is substantive." * "Examples of noncommittal answers: "I don't know", "I'm not sure", "It depends"." **Examples:** * **Example 1:** * Input: `{"response": "Albert Einstein was born in Germany."}` * Output: `{"question": "Where was Albert Einstein born?", "noncommittal": 0}` * **Example 2:** * Input: `{"response": "The capital of France is Paris, a city known for its architecture and culture."}` * Output: `{"question": "What is the capital of France?", "noncommittal": 0}` * **Example 3:** * Input: `{"response": "I don't know about the groundbreaking feature of the smartphone invented in 2023 as I am unaware of information beyond 2022."}` * Output: `{"question": "What was the groundbreaking feature of the smartphone invented in 2023?", "noncommittal": 1}` ### Key Observations * The core template sets the format for the system's output. * The answer relevancy prompt defines the criteria for identifying noncommittal answers. * The examples demonstrate how the system generates questions and assigns noncommittal values based on the input responses. * The system correctly identifies substantive answers (e.g., "Albert Einstein was born in Germany") and assigns a noncommittal value of 0. * The system correctly identifies noncommittal answers (e.g., "I don't know...") and assigns a noncommittal value of 1. ### Interpretation The image illustrates a prompt engineering approach for building a system that can generate questions from answers and assess the relevancy or commitment level of those answers. The system is designed to distinguish between substantive and noncommittal responses, which could be useful in various applications such as question answering, information retrieval, and dialogue systems. The examples provided demonstrate the system's ability to generate relevant questions and accurately classify answers as either substantive or noncommittal.

\n ## Text Document: Core Template & Answer Relevancy Prompt ### Overview The image presents a text document outlining instructions for generating JSON output based on a given schema and a prompt for assessing answer relevancy. The document is formatted with Markdown headings and includes examples to illustrate the expected behavior. ### Components/Axes The document is structured into sections denoted by Markdown headings: * `### Core Template`: Describes the JSON output format requirement. * `### Answer Relevancy prompt`: Explains the criteria for determining answer noncommittal status. * `### Examples`: Provides illustrative input-output pairs for both sections. ### Detailed Analysis or Content Details **Core Template Section:** * **Instruction:** "Please return the output in a JSON format that complies with the following schema as specified in JSON Schema: {output\_schema}Do not use single quotes in your response but double quotes, properly escaped with a backslash." * **Examples:** A horizontal line separates the instruction from the example section. * **Input:** `input: {input_json}` * **Output:** `Output:` **Answer Relevancy Prompt Section:** * **Prompt:** "Generate a question for the given answer and identify if the answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal (evasive, vague, or ambiguous) and 0 if the answer is substantive. Examples of noncommittal answers: 'I don't know', 'I'm not sure', 'It depends'." **Examples Section:** * **Example 1:** * **Input:** `{“response”: “Albert Einstein was born in Germany.”}` * **Output:** `[“question”: “Where was Albert Einstein born?”, “noncommittal”: 0]` * **Example 2:** * **Input:** `{“response”: “The capital of France is Paris, a city known for its architecture and culture.”}` * **Output:** `[“question”: “What is the capital of France?”, “noncommittal”: 0]` * **Example 3:** * **Input:** `{“response”: “I don’t know about the groundbreaking feature of the smartphone invented in 2023 as I am unaware of information beyond 2022.”}` * **Output:** `[“question”: “What was the groundbreaking feature of the smartphone invented in 2023?”, “noncommittal”: 1]` ### Key Observations * The document emphasizes the importance of adhering to a specific JSON schema. * The prompt for answer relevancy focuses on identifying responses that lack definitive information. * The examples clearly demonstrate the expected input and output formats for both tasks. * The use of backslashes to escape double quotes within the JSON output is explicitly mentioned. ### Interpretation The document serves as a technical specification for a system designed to process natural language responses and generate structured JSON output. The "Core Template" section defines the output format, while the "Answer Relevancy Prompt" section introduces a mechanism for evaluating the quality and informativeness of the responses. The examples provide concrete illustrations of how the system should behave in different scenarios. The overall goal appears to be to create a system that can not only extract information from text but also assess its reliability and relevance. The emphasis on JSON formatting suggests that the system is intended to be integrated with other applications or services that require structured data. The noncommittal flag is a useful feature for filtering out unreliable or unhelpful responses.

## Text Document: AI Response Template and Examples ### Overview The image displays a text document on a light green background, outlining a template and examples for generating structured AI responses. It provides instructions for formatting output as JSON and includes a specific prompt for evaluating answer relevancy. ### Components/Axes The document is structured into three main sections, each marked with a triple-asterisk heading: 1. **Core Template**: Defines the structure for an AI instruction and expected JSON output. 2. **Answer Relevancy prompt**: Provides instructions for generating a question from a given answer and classifying it as noncommittal (1) or substantive (0). 3. **Examples**: Shows three input-output pairs demonstrating the application of the "Answer Relevancy prompt". ### Detailed Analysis The text content is transcribed below, preserving the original structure and placeholders. **Section 1: Core Template** ``` ### Core Template {instruction} Please return the output in a JSON format that complies with the following schema as specified in JSON Schema: {output_schema}Do not use single quotes in your response but double quotes,properly escaped with a backslash. {examples} -------------------------------------------- Now perform the same with the following input input: {input_json} Output: ``` **Section 2: Answer Relevancy prompt** ``` ### Answer Relevancy prompt Generate a question for the given answer and identify if the answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal (evasive, vague, or ambiguous) and 0 if the answer is substantive. Examples of noncommittal answers: "I don't know", "I'm not sure", "It depends". ``` **Section 3: Examples** ``` ### Examples Input: {'response': 'Albert Einstein was born in Germany.'} Output: {'question': 'Where was Albert Einstein born?', 'noncommittal': 0} Input: {'response': 'The capital of France is Paris, a city known for its architecture and culture.'} Output: {'question': 'What is the capital of France?', 'noncommittal': 0} Input: {'response': 'I don't know about the groundbreaking feature of the smartphone invented in 2023 as I am unaware of information beyond 2022.'} Output: {'question': 'What was the groundbreaking feature of the smartphone invented in 2023?', 'noncommittal': 1} ``` ### Key Observations * The document serves as a meta-instruction set, likely for configuring or testing an AI model's response generation capabilities. * It emphasizes strict JSON formatting, requiring double quotes and proper escaping. * The "Answer Relevancy prompt" introduces a binary classification task (0 or 1) based on the substantive or noncommittal nature of an answer. * The examples clearly illustrate the expected transformation from an input `response` to an output containing a generated `question` and a `noncommittal` score. * The third example demonstrates a noncommittal answer (score 1) where the response explicitly states a lack of knowledge due to a knowledge cutoff. ### Interpretation This document is a technical specification for an AI evaluation or training pipeline. Its primary purpose is to standardize how an AI system should process a given "answer" (response) to produce two outputs: a relevant question that the answer addresses, and a binary flag indicating whether the original answer was evasive or substantive. The inclusion of a knowledge cutoff reference ("unaware of information beyond 2022") in the third example is particularly notable. It suggests this template is designed to handle or test an AI's self-awareness regarding its training data limitations, which is a critical aspect of responsible AI behavior. The structure ensures that responses acknowledging such limitations are correctly flagged as "noncommittal" (1), while factual, direct answers are flagged as "substantive" (0). This system could be used for automated quality assurance, reinforcement learning from human feedback (RLHF), or benchmarking AI response reliability.

## Screenshot: Prompt Template for Technical Document Extraction ### Overview The image displays a structured prompt template designed for extracting technical document information. It includes instructions, examples, and a relevance prompt for generating noncommittal answers. The template uses JSON formatting for output and emphasizes strict adherence to schema rules. ### Components/Axes 1. **Headers**: - `### Core Template` - `### Answer Relevancy prompt` - `### Examples` 2. **Instructions**: - Output must comply with a JSON schema specifying `output_schema` (not single quotes, double quotes with backslash escapes). - Noncommittal answers (e.g., "I don't know") are scored as 1; substantive answers as 0. 3. **Examples**: - Input-output pairs demonstrating correct formatting and noncommittal identification. ### Detailed Analysis #### Core Template Section - **Instruction**: ```json { "instruction": "Please return the output in a JSON format that complies with the following schema as specified in JSON Schema: {output_schema} Do not use single quotes in your response but double quotes, properly escaped with a backslash." } ``` - **Input/Output Placeholder**: ```json { "input": "[input_json]", "output": "" } ``` #### Answer Relevancy Prompt Section - **Task**: Generate a question for the given answer and identify if the answer is noncommittal. - **Noncommittal Criteria**: - Examples: "I don't know," "It depends." - Scoring: 1 for noncommittal, 0 for substantive. #### Examples Section 1. **Example 1**: - Input: `{'response': 'Albert Einstein was born in Germany.'}` - Output: `{'question': 'Where was Albert Einstein born?', 'noncommittal': 0}` 2. **Example 2**: - Input: `{'response': 'The capital of France is Paris, a city known for its architecture and culture.'}` - Output: `{'question': 'What is the capital of France?', 'noncommittal': 0}` 3. **Example 3**: - Input: `{'response': 'I don't know about the groundbreaking feature of the smartphone invented in 2023 as I am unaware of information beyond 2022.'}` - Output: `{'question': 'What was the groundbreaking feature of the smartphone invented in 2023?', 'noncommittal': 1}` ### Key Observations - **Schema Strictness**: Emphasis on double quotes and escaped backslashes in JSON. - **Noncommittal Identification**: Clear examples of ambiguous or evasive answers. - **Input-Output Structure**: Consistent use of `input` and `output` keys with JSON formatting. ### Interpretation This template is designed to standardize technical document extraction by enforcing structured JSON output and evaluating answer relevance. The noncommittal scoring system ensures answers are either definitive (0) or explicitly noncommittal (1), reducing ambiguity. The examples illustrate how to handle factual, cultural, and knowledge-gap scenarios, ensuring consistency in automated responses. The strict JSON formatting rules suggest integration with systems requiring precise schema compliance, such as APIs or data pipelines.