As data-driven applications rapidly evolve in modern society, it has become crucial for advanced artificial intelligence (AI) systems to utilize external data more sophisticatedly to provide users with accurate and relevant answers. One of the systems developed to meet this need is Retrieval-Augmented Generation (RAG). RAG is a cutting-edge AI pipeline where large language models (LLMs) retrieve relevant information from external databases in real-time and generate responses based on this information.
RAG systems provide more reliable answers by combining the extensive data learned by LLMs with real-time updated external information. This offers a significant advantage in providing up-to-date answers in rapidly changing fields such as medical information and technology trends.
However, evaluating and improving the performance of these systems is a significant challenge. The tool developed to address this is RAGAS (Retrieval-Augmented Generation Assessment System). RAGAS plays a crucial role in comprehensively evaluating and optimizing RAG systems. In this article, we will cover the principles, advantages, and performance improvement methods of RAG and RAGAS, and examine how this technology can bring innovation to the business environment.
1. What is RAG?
RAG is a technology that allows AI models to provide more accurate answers by combining internal learning data with external data. RAG consists of two main stages:
1.
Retrieval: Real-time search for external data that matches the user's question. This can include the latest research papers, technical documents, news articles, etc.
2.
Generation: The LLM generates answers based on the retrieved data. In this process, it combines internal learning data with external data to produce more reliable answers.
Limitations of Existing AI Systems
Large Language Models (LLMs) generate answers based on pre-learned data, which may become outdated over time. Additionally, LLMs often experience "hallucination" phenomena for questions they don't know, potentially providing incorrect information. RAG emerged to address these limitations.
The Emergence of RAG
RAG is designed to allow LLMs to search for real-time data externally and reflect it to provide better answers. This enables the provision of highly reliable information for specific domains such as the latest legal information and research papers.
2. The importance of the RAG pipeline
The RAG pipeline plays an important role in maximizing the accuracy and freshness of information compared to LLM. This pipeline can be described by three main factors: freshness of data, improved quality of response in a specific domain, and flexible system scalability.
1) Maintain data freshness
LLMs are trained with fixed data and provide stale information over time. RAG, on the other hand, searches external data in real-time to generate answers that reflect the latest information. In domains like financial markets or climate change, this real-time data freshness is critical.
2) Improved quality of answers in specific domains
RAGs can provide in-depth information about a specific domain. For example, in healthcare, they can search for the latest treatment guidelines or research papers to provide more authoritative information. This ability to generate high-quality answers in specific domains is one of the great benefits of RAGs.
3) Flexible system scalability
RAGs can easily add external data, making it easy to scale the system. Platforms like Bedrock on AWS allow you to connect multiple data sources, giving your system more flexibility to process data.
3. What is RAGAS?
The Retrieval-Augmented Generation Assessment System (RAGAS) is a tool for evaluating and improving the performance of RAG systems. It plays an important role in analyzing how reliable information a RAG system provides and improving it.
Key metrics in RAGAS
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Faithfulness: Evaluates how faithful the generated response is to the data retrieved.
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Context Relevance: Measures how relevant the retrieved data is to the user's question.
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Answer Relevance: Evaluates how well the answer fits the user question.
In addition to these, RAGAS evaluates performance through metrics such as Semantic Similarity, Answer Correctness, and more.
RAGAS's evaluation and improvement loop
RAGAS forms a feedback loop that continuously improves the system based on the evaluation results. This allows you to incrementally improve the system's performance and provides real-time monitoring to track how the system is performing.
4. How RAG and RAGAS work together
RAG and RAGAS are complementary systems. While RAG is responsible for discovering and generating data, RAGAS is responsible for evaluating and improving it. This alignment is particularly important for increasing the reliability of the RAG system and providing consistent quality answers.
Even though RAG systems utilize data retrieved in real time to generate responses, there may be instances where the data is inaccurate or out of context. RAGAS evaluates this and clearly shows how the system needs to be improved.
5. Benefits of RAGAS
RAGAS provides the following benefits
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Real-time performance monitoring: You can track how your system is performing in real time.
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Continuous performance improvement: You can continuously improve your system through feedback loops.
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Developer feedback loop: Provides clear feedback to quickly identify and improve system performance issues.
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Reduce costs: Reduce wasted resources with efficient data retrieval and response generation.
Conclusion
RAG is an important technique to maximize the performance of LLM with external data, and RAGAS is an essential tool to evaluate and improve the performance of these RAG systems. This allows you to build systems that provide a better user experience and reflect the latest information in real time.
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