Why Retrieval Augmented Generation is the Next Big Thing in AI
Elon Musk once wrote: “The pace of progress in Artificial Intelligence (AI) is incredibly fast.”1 India’s AI market is expected to grow at a breakneck pace of 25-35% till 2027.2 New frameworks are emerging almost every other day to enhance the efficiency and accuracy of AI models. Retrieval augmented generation is one such framework that is gaining traction because it is transforming the way AI models interact with data and generate responses. So, let’s take a look at the intricacies of the retrieval augmented generation, explore its benefits, and learn why companies must keep a close eye on this trend.
What is Retrieval Augmented Generation?
Retrieval augmented generation is an advanced method that combines two techniques— retrieval and generation. It helps improve the accuracy of generated content, including its relevance. The method is handy for any model providing up-to-date or highly detailed information, particularly applications like question-answering systems, document summarization, and chatbots. Here are some benefits:
1. Improves Accuracy
The generated output is accurate since retrieval draws from fresh and authoritative sources. It helps address one of the limitations of traditional language models that rely on fixed training data.
2. Offers Scale
The use of retrieval augmented generation allows access to large, dynamic datasets without retraining the model for every new piece of information.
3. Ensures Factual Consistency
A retrieval-based approach reduces the chances of generating hallucinations (e.g., incorrect or made-up facts) that are common in purely generative models.
4. Uses Resources Efficiently
The framework’s reliance on retrieval means it doesn’t need to encode all the knowledge directly into the generative model. Thus, it reduces the model size and the computational resources needed for training and updating the model.
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What is the Retrieval Augmented Generation Pattern?
The retrieval augmented generation pattern is a useful technique that leverages the strengths of both retrieval-based and generative AI models. It involves two primary steps:
1. Retrieval
The framework first retrieves relevant information from a corpus of text, documents, or databases. Advanced techniques like semantic search or vector databases power this process. They can identify the most pertinent information based on the query.
2. Generation
A generative AI model generates a comprehensive and informative response after the retrieval. The retrieved knowledge is incorporated into the generation process to ensure that the response contains facts, and avoids hallucinations or nonsensical output.
3. Types of Retrieval Mechanisms
A. Open-Domain Retrieval
The retrieval system can search a broad range of general or specific domains, making it suitable for open-ended queries.
B. Closed-Domain Retrieval
This is limited to a specific, constrained domain where domain relevance is critical for the generation step. For example, company-specific knowledge bases, medical literature, etc.
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Challenges of Retrieval Augmented Generation
Retrieval augmented generation involves certain challenges that need to be addressed for effective implementation. These challenges span both technical and practical aspects of the system. Let’s check them out:
1. Quality and Relevance
There is a likelihood that the system returns irrelevant or low-quality documents as the framework relies heavily on the quality of the external knowledge base. The results can be inaccurate or misleading if the retrieved data is incorrect or outdated.
2. Latency
A system can experience delays, especially if it is obtaining information from a massive database. The delay can slow down the overall response time, making it unsuitable for real-time applications.
3. Engineering Complexity
It is difficult to build RAG systems and maintain them, compared to standalone generative models. They require the integration of retrieval mechanisms with generative models, leading to challenges in synchronization, error handling, and optimization.
4. Lack of Interpretability
There is a risk of limited transparency with retrieval systems, even though they provide references. It is hard for users to understand how the model arrives at certain outputs when multiple sources are fused, and verify the accuracy of individual claims subsequently.
5. Privacy
Retrieval systems can likely tap an external knowledge base that includes sensitive or proprietary information. It is a serious concern if retrieval is not secure, and unauthorized access can result in criminal charges and hefty penalties in fields like healthcare or law.
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Does ChatGPT Use Retrieval Augmented Generation?
ChatGPT in its standard form does not use retrieval augmented generation directly. The chatbot relies on a pre-trained generative model. In other words, it generates responses based on patterns picked up from a large corpus of text in training.
The corpus consists of a wide range of information available up until a specific cutoff date (October 2023). However, it doesn’t retrieve information in real-time or from external knowledge sources.
It is important to note that ChatGPT does use a form of RAG. The users of ChatGPT’s paid version have access to web-browsing capabilities, allowing the model to search the internet in real time.
OpenAI has also introduced plugins that allow ChatGPT to interact with external databases, APIs, and other tools. Thus, augmenting its capabilities by retrieving data from specific sources. These plugins enable RAG functionality in a more specialized and task-specific way.
The features allow ChatGPT to look up new information during a conversation. It is still different from a fully integrated RAG system in structure and design. It prevents ChatGPT’s information from becoming outdated as it accesses new data after its last training update.
There is a possibility that future versions of ChatGPT will adopt a comprehensive RAG approach.
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What is the Difference Between Fine Tuning and Retrieval Augmented Generation?
There are several differences between fine-tuning and RAG even though they are techniques to enhance the capabilities of AI models. Fine-tuning is like training a dog to fetch a specific toy, whereas RAG is like obtaining information from a library to write a report.
1. Focus
Fine-tuning focuses on adapting a model’s general knowledge to a specific task, while RAG focuses on retrieving and incorporating relevant information for each query.
2. Process
The former deals with feeding the model with a large dataset of task-specific examples. It concerns adjusting the model’s parameters to minimize the error between its predicted output and the correct one. A model using RAG retrieves information from a knowledge base based on the query and generates a response.
3. Data
Fine-tuning uses task-specific data to adjust the model’s parameters. In contrast, RAG uses a knowledge base to retrieve information.
4. Output
Fine-tuning produces responses based on the model’s learned knowledge. RAG produces responses based on the retrieved information and the model’s generative capabilities.
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