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A complete prompt for a Retrieval-Augmented Generation (RAG) system typically combines three key components: an instruction, the user's question, and the retrieved context. The instruction guides the model on how to utilize the provided information. This is followed by the user's query. Finally, the retrieved texts are presented, often introduced by a transitional phrase like `The information regarding this question is given as follows:`, to provide the necessary context for generating an answer.

Structure of a Complete RAG Prompt for Question Answering

This prompt instruction exemplifies a technique to enhance an LLM's reliability. It explicitly commands the model to prioritize factual accuracy and faithfulness to the provided context. Furthermore, it provides a clear directive for the model to abstain from answering by outputting 'No answer!' when the given information is insufficient to formulate an accurate response. The instruction is as follows:

`Please note that your answers need to be as accurate as possible and faithful to the facts. If the information provided is insufficient for an accurate response, you may simply output "No answer!".`

Example Prompt Instruction for Faithfulness and Abstention

A company's internal Q&A bot is designed to answer employee questions using only official company documents. However, employees have noticed that the bot sometimes provides answers based on its general, pre-existing knowledge, leading to outdated or incorrect information. Below is the typical structure of the message sent to the language model:

`User Question: [The employee's question is inserted here.]
Retrieved Context: [Relevant text from an official company document is inserted here.]`

Based on the standard components of a complete and effective prompt for such a system, identify the key component that is missing and explain how its absence leads to the observed problem.

Diagnosing a Faulty Q&A System Prompt

A core component of a prompt for Retrieval-Augmented Generation involves setting explicit instructions for the model. This includes defining the primary task, such as answering a question, and grounding the model by directing it to base its response exclusively on the provided texts.

Task Definition and Grounding in RAG Prompts

You are building a question-answering system. Given the components below, construct a single, complete prompt that follows the standard structure for this task. Your prompt must integrate all three necessary elements: a guiding instruction, the user's query, and the provided information.

*   **User's Query:** "What is the primary function of a transformer's self-attention mechanism?"
*   **Retrieved Information:** "The self-attention mechanism allows a model to weigh the importance of different words in an input sequence when processing that sequence. It helps the model to understand context and relationships between words, even if they are far apart."

Constructing a RAG Prompt

A developer is constructing a message to send to a large language model to answer a user's question based on a retrieved document. The developer has the following three components:

1.  **The Instruction:** `You are a helpful assistant. Answer the user's question based *only* on the provided text. If the information is not in the text, state that the answer cannot be found.`
2.  **The User's Question:** `What is the boiling point of ethanol?`
3.  **The Retrieved Context:** `Ethanol, a volatile, 

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