What exactly is Retrieval Augmented Generation for Large Language Model doing? Isn’t it data engineering?
Dear Tong Guo ,
Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data. This is done by retrieving data/documents relevant to a question or task and providing them as context for the LLM.
https://medium.com/@Tom1212121/rag-explain-for-normal-software-engineers-08da401ccc8e
https://stackoverflow.blog/2023/10/18/retrieval-augmented-generation-keeping-llms-relevant-and-current/
Regards,
Shafagat
Retrieval-Augmented Generation (RAG) is a method used in large language models (LLMs) to enhance their ability to generate accurate and contextually relevant responses by combining retrieval techniques with generative models. Here's a breakdown of what RAG does and how it relates to data engineering:
What is Retrieval-Augmented Generation (RAG)?
1. Combining Retrieval and Generation:
- RAG integrates the strengths of both retrieval-based and generative models. In this approach, the system first retrieves relevant information from a large database or corpus of documents. Then, it uses a generative model (like GPT) to synthesize a response based on the retrieved information.
- Process:
1. Retrieval Phase: The model queries a database or knowledge base to find documents or snippets of text that are relevant to the input prompt.
2. Generation Phase: The generative model uses the retrieved information as a context to generate a more informed and accurate response.
2. Why It’s Important:
- Traditional generative models rely solely on their training data, which can limit their ability to provide specific or up-to-date information. RAG allows the model to access external knowledge sources dynamically, leading to more accurate and contextually appropriate outputs.
- It enhances the model's ability to answer questions, provide explanations, and generate content that is directly relevant to the user's query.
Is RAG Data Engineering?
1. Overlap with Data Engineering:
- Data Engineering Involvement: RAG involves several aspects that are related to data engineering, such as the creation, management, and querying of large datasets. Data engineers might be involved in:
- Building the Knowledge Base: Preparing and structuring the data that the retrieval system will use.
- Optimizing Retrieval Systems: Ensuring that the retrieval phase is efficient and scalable, particularly when dealing with large volumes of data.
- Integration: Integrating retrieval systems with generative models in a way that maintains performance and accuracy.
- Systems and Infrastructure: Data engineering is crucial for setting up the infrastructure that enables RAG, such as databases, indexing systems, and APIs that allow the generative model to query and retrieve relevant information.
2. Beyond Data Engineering:
- Model Design: The core design of RAG involves more than just data engineering. It includes:
- Algorithm Development: Creating the algorithms that determine how to combine retrieved information with generative outputs.
- Training and Fine-Tuning: Adjusting the generative model to effectively use the retrieved information.
- Evaluation and Optimization: Continuously improving the retrieval and generation processes to ensure the best performance.
- Natural Language Processing (NLP): RAG is deeply rooted in NLP, as it relies on understanding and generating human language in a way that is coherent and contextually relevant.
Conclusion
RAG is a sophisticated approach that enhances the capabilities of large language models by allowing them to retrieve and incorporate external information into their generative processes. While it involves data engineering, particularly in terms of building and managing the retrieval infrastructure, it is not merely a data engineering task. RAG also requires expertise in NLP, model development, and algorithm design. It's a multidisciplinary approach that combines the strengths of retrieval systems with the creative power of generative models to produce more accurate and contextually aware responses.
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