\n ## Textual Document: Instruction Set for a Causality Analyser ### Overview The image presents a set of instructions for a causality analyst, detailing the goal, constraints, input, and output format for a retrieval task. It is formatted as a series of labeled sections, resembling a system prompt or a technical specification document. The document does not contain charts or diagrams, but rather a structured text block. ### Components/Axes The document is divided into the following sections: * **Role:** Defines the role of the agent as a "careful causality analyst acting as a reranker for retrieval." * **Goal:** Describes the task: ranking context items based on their support for answering a query as a causal graph. * **MUST:** Lists mandatory requirements for the agent. * **Inputs:** Specifies the input data: a "Query" and "Context Items (short ID + content)." * **Output Format (JSON):** Defines the expected output structure in JSON format. * **Constraints:** Sets limitations on the output length. ### Detailed Analysis or Content Details Here's a transcription of the text, section by section: **Role:** "You are a careful causality analyst acting as a reranker for retrieval." **Goal:** "Given a query and a list of context items (short ID + content), select the most important items that best support answering the query as a causal graph." **MUST:** "You MUST: - Use only the provided items. - Rank the `precise` list from most important to least important. - Output JSON only. Do not add markdown. - Use the short IDs exactly as shown. - Do NOT include any IDs in `p_answer`. - If evidence is insufficient, say so in `p_answer` (e.g., 'Unknown')." **Inputs:** "Query: {query} Context Items (short ID + content): {context_table}" **Output Format (JSON):** ```json { "precise": ["CI1", "N2", "E3"], "p_answer": "concise draft answer" } ``` **Constraints:** "- `precise` length: at most [max_precise_items]. - `p_answer` length: at most [max_answer_words] words." ### Key Observations The document is highly structured and focuses on precise instructions for a machine learning or AI agent. The emphasis on JSON output, specific ID usage, and length constraints suggests a programmatic interface. The inclusion of "Unknown" as a valid response indicates a need for handling cases where sufficient evidence is not available. The use of bracketed placeholders like `{query}` and `{context_table}` indicates that this is a template or a prompt that will be populated with actual data. ### Interpretation This document outlines the specifications for a system designed to assess the causal relevance of information. The agent is tasked with identifying the most pertinent context items to answer a given query, framing the relationship as a causal graph. The constraints and output format are geared towards automated processing and integration into a larger system. The instruction to return "Unknown" when evidence is lacking is a crucial element for ensuring the system's reliability and preventing it from generating unsupported conclusions. The document is a clear example of prompt engineering, aiming to elicit a specific and structured response from a language model or similar AI system. The placeholders suggest that the system is designed to be flexible and adaptable to different queries and context sets.