Feb 6, 2024

How organizations can improve the accuracy of their RAG Systems

This blog post aims to help organizations improve accuracy of their current RAG systems increasing trust and helping them become more flexible

Improve accuracy, RAG Systems, How organizations use AI, RAG accuracy, How to use RAG

Retrieval-Augmented Generation (RAG) is a technique that combines information retrieval with text generation, allowing AI models to retrieve relevant information from a knowledge source and incorporate it into generated text. RAG can enhance the accuracy and reliability of generative AI models, such as chatbots, by fetching facts from external sources, such as a knowledge base or a search engine.

However, RAG is not a silver bullet for all generative AI problems. Depending on the use case, RAG may have some limitations or challenges, such as:

  • The quality and relevance of the retrieved information depends on the information retrieval system and the knowledge source. If the information retrieval system is not well-designed or the knowledge source is not authoritative, comprehensive, or up-to-date, the generated text may be inaccurate, misleading, or outdated.
  • The integration of the retrieved information and the generated text may not be seamless or natural. Sometimes, the retrieved information may not match the context or the tone of the generated text, resulting in awkward or inconsistent responses.
  • The scalability and performance of the RAG system may be affected by the complexity and size of the information retrieval system and the knowledge source. If the information retrieval system or the knowledge source is too large or too slow, the RAG system may take longer to generate responses or consume more resources.

Therefore, improving the accuracy of your RAG system requires careful attention to the following aspects:

Information Retrieval System

The information retrieval system is responsible for finding and ranking the most relevant documents or passages from the knowledge source, given a user query. The information retrieval system can be based on different methods, such as keyword matching, vector similarity, or hybrid search.

Some of the techniques that can improve the information retrieval system are:

  • Embedding finetuning: This involves training a neural network to produce embeddings (vector representations) of the documents or passages that capture their semantic meaning and relevance to the query. Embedding finetuning can improve the quality and diversity of the retrieved information, as well as reduce the size and complexity of the vector space.
  • Metadata attachment: This involves adding additional information, such as tags, categories, or timestamps, to the documents or passages, and using them as filters or boosters in the retrieval process. Metadata attachment can help the information retrieval system to focus on the most relevant and recent information, as well as to handle ambiguous or complex queries.
  • Hybrid search: This involves combining different retrieval methods, such as keyword matching, vector similarity, and query expansion, to leverage their strengths and overcome their weaknesses. Hybrid search can increase the recall and precision of the information retrieval system, as well as handle diverse and dynamic queries.

Knowledge Source

The knowledge source is the collection of documents or passages that provide grounding data for the generative AI models. The knowledge source can be based on different sources, such as Wikipedia, news articles, or domain-specific corpora.

Some of the techniques that can improve the knowledge source are:

  • Data cleaning: This involves removing or correcting errors, inconsistencies, or redundancies in the data, such as spelling mistakes, grammatical errors, or duplicated information. Data cleaning can improve the quality and reliability of the knowledge source, as well as reduce the noise and confusion in the retrieval and generation process.
  • Data augmentation: This involves adding or enriching the data with new or missing information, such as synonyms, antonyms, or related terms. Data augmentation can improve the coverage and diversity of the knowledge source, as well as increase the variety and richness of the generated text.
  • Data updating: This involves monitoring and updating the data with the latest and most relevant information, such as new facts, events, or trends. Data updating can improve the timeliness and freshness of the knowledge source, as well as keep the generated text aligned with the current state of the world.

Text Generation System

The text generation system is responsible for producing natural and coherent text that answers the user query, given the retrieved information. The text generation system can be based on different models, such as LLAMA, GPT, or MISTRAL.

Some of the techniques that can improve the text generation system are:

  • Model finetuning: This involves training a pre-trained model on a specific task or domain, such as question answering, summarization, or dialogue. Model finetuning can improve the accuracy and fluency of the text generation system, as well as adapt it to the user’s needs and preferences.
  • Model fusion: This involves combining different models or model components, such as encoder, decoder, or attention, to leverage their complementary features and capabilities. Model fusion can improve the flexibility and robustness of the text generation system, as well as enhance its performance and functionality.
  • Model evaluation: This involves measuring and monitoring the quality and effectiveness of the text generation system, using different metrics, such as BLEU, ROUGE, or human evaluation. Model evaluation can help the text generation system to identify and correct its errors, weaknesses, or biases, as well as to improve its user satisfaction and trust.

Building an inhouse flexible and accurate RAG Systems is a little tricky. We at Fluid AI stand at the forefront of this AI revolution, helping organizations kickstart their AI journey helping them deploy a production ready RAG system within hours. We’re committed to making your organization future-ready, just like we’ve done for Mastercard, Bank of America, Warren Buffet and other top fortune 500 companies.

Take the first step towards this exciting journey by booking a free demo call with us today. Let’s explore the possibilities together and unlock the full potential of AI for your organization. Remember, the future belongs to those who prepare for it today.

Decision pointsOpen-Source LLMClose-Source LLM
AccessibilityThe code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation.The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer.
CustomizationLLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques.Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer.
Community & DevelopmentBenefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements.Development is controlled by the owning company, with limited external contributions.
SupportSupport may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance.Typically comes with dedicated support from the developer, offering professional assistance and guidance.
CostGenerally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance.May involve licensing fees, pay-per-use models or require cloud-based access with associated costs.
Transparency & BiasGreater transparency as the training data and methods are open to scrutiny, potentially reducing bias.Limited transparency makes it harder to identify and address potential biases within the model.
IPCode and potentially training data are publicly accessible, can be used as a foundation for building new models.Code and training data are considered trade secrets, no external contributions
SecurityTraining data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the communityThe codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment
ScalabilityUsers might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resourcesCompanies often have access to significant resources for training and scaling their models and can be offered as cloud-based services
Deployment & Integration ComplexityOffers greater flexibility for customization and integration into specific workflows but often requires more technical knowledgeTypically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor.
10 ponits you need to evaluate for your Enterprise Usecases

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