Retrieval Augmented Generation
1. Overview
1.1. Definition:
- A framework that combines machine learning models to enhance information retrieval and text generation capabilities.
- It integrates two predominant AI tasks: retrieval of relevant data from a knowledge base and subsequent generation of a coherent response or narrative based on that data.
1.2. Key Components:
- Retriever Model:
- Generally based on models like BERT, designed to extract relevant documents or data chunks from a large corpus.
- Utilizes querying techniques to identify information pertinent to the user’s question or topic.
- Generator Model:
- Typically a language model such as GPT, tasked with creating natural language output from the retrieved information.
- Ensures that the final response is coherent, contextually relevant, and aligns with human-like language quality.
1.3. Applications:
- Frequently used in conversational AI, customer service, and content creation to provide detailed, context-aware responses.
- Enhances research by providing a systemic way to retrieve and summarize knowledge from expansive datasets or articles.
1.4. Challenges:
- Accuracy in retrieval to ensure the generator has the most relevant and up-to-date information.
- Balancing the generation of creative language with factual correctness.
- Managing computational efficiency to handle the typically large models involved in such frameworks.
1.5. Connections to Other Domains:
- Similar to traditional search engines but advances the capability by integrating generative responses.
- Reflects advancements in NLP and AI where discrete models for retrieval and generation are continuously being refined and integrated.
2. Benefits
- limits context for the LLM to generate an answer
- can clearly reference sources for different aspects of the query
2.3. Specific and Up to Date Data
2.4. Lesser of a black box
- lower reliance on the condesnsed memory of an LLM