Prompting LLMs with Retrieved Texts in RAG
In the Retrieval-Augmented Generation (RAG) framework, the final prediction is generated by prompting a Large Language Model (LLM). This process involves feeding the model an input that combines both the original user query and the relevant texts retrieved from an external information source.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Prompting LLMs with Retrieved Texts in RAG
A system is designed to answer questions using a two-step process: first, it finds relevant documents from a database, and second, it uses a large language model to generate a final answer. A user asks, 'What is the battery life of the new "Innovate X" phone?' The system retrieves the following text: 'The Innovate X phone features a 5000mAh battery, providing up to 48 hours of talk time.' Which of the following inputs to the language model is structured to produce the most accurate and relevant final answer?
Diagnosing a Generation Failure in an Information System
Evaluating Generation Strategies in a Q&A System
Prompt Example for Synthesizing Answers from Provided Context
Restricting LLM Answers to Provided Text
Prompting LLMs with Retrieved Texts in RAG
A developer provides a language model with a specific piece of information:
Context: 'The fictional city of Aeridor was founded in the year 982 by Queen Elara.'The developer then asks the model:Question: 'When was Aeridor founded?'The model, however, responds with:Answer: 'Aeridor was founded in the 12th century.'Which of the following statements best analyzes the most likely reason for the model's incorrect response, despite being given the correct information?Internal Knowledge Base Chatbot Design
A developer is building a customer support chatbot that must answer questions using only the information from the company's official 'Return Policy' document to avoid providing inaccurate or outdated advice. Which of the following prompt strategies is the most effective for constraining the model's output to the provided text?
Learn After
Example Question for RAG-Based Answering
Challenge of Inaccurate Text Retrieval in RAG
Controlling LLM Dependency on Retrieved Context in RAG
Challenge of Developing a Universal Prompting Strategy for RAG
Structure of a Complete RAG Prompt for Question Answering
A system is designed to answer user questions by first finding a relevant text and then using a language model to generate a response based only on the information within that text. A user asks, 'What are the primary health benefits of regular exercise?' The system retrieves the following text: 'Consistent physical activity strengthens the heart muscle, which improves cardiovascular efficiency and lowers the risk of heart disease. It also aids in weight management by burning calories.' Which of the following generated answers best demonstrates the language model correctly performing its task?
A developer is building a system to answer user questions using retrieved information. For the user query 'What are the key differences between llamas and alpacas?', the system retrieves the following text: 'Llamas and alpacas are both South American camelids. Llamas are significantly larger, often weighing up to 400 pounds, while alpacas are smaller, typically under 200 pounds. A key distinguishing feature is their ears; llamas have long, banana-shaped ears, whereas alpacas have short, spear-shaped ears. Furthermore, llamas are primarily used as pack animals due to their size and strength, while alpacas are bred for their fine, luxurious fiber.' Which of the following represents the most effective and well-structured input to send to the language model to generate the final answer?
Analyzing an Erroneous Answer in a Retrieval-Based System
LLM Refusal to Answer due to Insufficient or Irrelevant Context