## Document Screenshot: Retrieval Reranker Prompt Template
### Overview
The image displays a structured text document, likely a system prompt or task specification for an AI model. It defines a specific role, goal, inputs, output format, and constraints for a retrieval reranking task focused on causal graph construction. The document is presented on a light blue background with black text, using section headers demarcated by lines of dashes.
### Components/Sections
The document is organized into five distinct sections:
1. **Role**: Defines the persona for the AI.
2. **Goal**: States the primary objective and mandatory requirements.
3. **Inputs**: Specifies the data provided to the model.
4. **Output Format (JSON)**: Defines the exact structure of the required response.
5. **Constraints**: Lists limitations on the output.
### Content Details (Full Transcription)
**---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**.
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": ["C1", "N2", "E3"],
"p_answer": "concise draft answer"
}
```
**---Constraints---**
- `precise` length: at most {max_precise_items} items.
- `p_answer` length: at most {max_answer_words} words.
### Key Observations
* **Template Nature**: The document contains placeholders (`{query}`, `{context_table}`, `{max_precise_items}`, `{max_answer_words}`), indicating it is a reusable template where specific values are inserted at runtime.
* **Specific Task Focus**: The goal is not general retrieval but specifically selecting items to support building a **causal graph**, implying a focus on cause-effect relationships.
* **Strict Output Control**: The instructions are highly prescriptive, mandating JSON-only output, exact ID usage, and prohibitions against including IDs in the answer field or adding markdown formatting.
* **Evidence Handling**: There is a clear protocol for handling insufficient evidence, requiring the answer field to state "Unknown" rather than guessing or omitting the field.
* **Ranking Requirement**: The `precise` list must be ordered from most to least important, adding a layer of evaluative judgment beyond simple selection.
### Interpretation
This document is a technical specification for a **causal retrieval-augmented generation (RAG) component**. It outlines the logic for a reranker that filters and orders retrieved context items based on their relevance to constructing a causal explanation for a given query.
The design reveals several underlying principles:
1. **Causal Reasoning Priority**: The system is engineered to prioritize information that can map onto nodes and edges of a causal graph (e.g., "C1", "N2", "E3" in the example output likely stand for Cause 1, Node 2, Effect 3).
2. **Deterministic Output**: By enforcing a strict JSON schema and forbidding markdown, the output is made machine-readable and predictable for downstream processing.
3. **Resource Management**: The constraints on list and answer length (`max_precise_items`, `max_answer_words`) are practical limits to control token usage, processing time, and the conciseness of the final answer.
4. **Auditability**: Requiring the use of exact short IDs allows for traceability back to the original source content within the context table.
In essence, this template defines a critical middleware step in a pipeline that moves from a broad query to a structured, causal explanation by intelligently selecting and ranking supporting evidence.
