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Chapter 9: Context-Aware Reasoning Applications using RAG and Agents

Questions and Answers

Q: What limitations do large language models face in context-aware reasoning?

A: Large language models (LLMs) face challenges in having accurate and current knowledge, leading to issues like hallucination and knowledge cutoff.

Q: How does hallucination and knowledge cutoff impact model accuracy?

A: Hallucination leads to the generation of incorrect or irrelevant information, while knowledge cutoff limits the model's understanding to information available up to a certain date, impacting the accuracy of responses.

Q: What is retrieval-augmented generation (RAG), and how does it work?

A: RAG is a framework that provides LLMs access to data they did not see during training. It overcomes knowledge limitations by allowing LLM-powered applications to use external data sources.

Q: How do external sources of knowledge contribute to RAG?

A: External sources of knowledge in RAG provide additional data not contained within the LLM's parametric memory, helping to mitigate issues like hallucination and knowledge cutoff.

Q: What is the significance of document loading and chunking in RAG?

A: Document loading and chunking are important in RAG for organizing and processing external data, making it accessible for the model to enhance its knowledge base and reasoning capabilities".

Q: Can you explain the RAG workflow and its implementation?

A: The RAG workflow involves integrating external data sources with LLMs, using processes like document loading, chunking, and retrieval-augmented methods to enhance the model's responses with additional, relevant information.

Q: What are the key considerations in developing context-aware reasoning applications?

A: Key considerations include managing the accuracy of knowledge, updating information regularly, and integrating external data sources effectively to address hallucination and knowledge cutoff issues.

Q: How does embedding vector store and retrieval affect RAG's performance?

A: Embedding vector storage and retrieval are crucial in RAG for efficiently managing and accessing relevant external data, which significantly enhances the model's performance by providing additional context and information.

Q: What are some effective strategies for reranking with maximum marginal relevance?

A: Effective strategies include using algorithms that prioritize relevance and diversity in the retrieval results, ensuring that the most pertinent and varied information is presented in response to queries.

Chapters

  • Chapter 1 - Generative AI Use Cases, Fundamentals, Project Lifecycle
  • Chapter 2 - Prompt Engineering and In-Context Learning
  • Chapter 3 - Large-Language Foundation Models
  • Chapter 4 - Quantization and Distributed Computing
  • Chapter 5 - Fine-Tuning and Evaluation
  • Chapter 6 - Parameter-efficient Fine Tuning (PEFT)
  • Chapter 7 - Fine-tuning using Reinforcement Learning with RLHF
  • Chapter 8 - Optimize and Deploy Generative AI Applications
  • Chapter 9 - Retrieval Augmented Generation (RAG) and Agents
  • Chapter 10 - Multimodal Foundation Models
  • Chapter 11 - Controlled Generation and Fine-Tuning with Stable Diffusion
  • Chapter 12 - Amazon Bedrock Managed Service for Generative AI

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