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RAG (Retrieval-augmented generation) in AI is a technique for enhancing the accuracy and reliability of large language models (LLMs). It does this by allowing the LLM to "consult" or integrate with external knowledge base for relevant information before generating a response. It uses a retriever to select relevant documents from a large corpus and a generator to produce text based on the retrieved documents and the input query.
Think of it as giving the LLM a personal library to check facts and details before speaking. This RAG technology generate more accurate and reliable outputs by grounding LLM in factual information with external sources of knowledge like documents or databases of organisation.
RAG technology is basically LLM with real-time data, this combination expands the range of applications in the area which require factual accuracy, relevance, and reducing the risk of transferring inaccurate information.
GPT focuses on internal language modeling. It learns the statistical relationships between words and phrases within a massive dataset of text and code. This allows it to generate creative text formats like poems, code, scripts, musical pieces, and more.
RAG focuses on external knowledge integration. It leverages a pre-trained language model (like a GPT) but also consults external knowledge sources like documents or databases. This allows it to provide more factual and grounded responses based on real-world information.
GPT typically has a single-stage architecture. The input prompt is fed directly to the model, and it generates an output based on its internal knowledge of language patterns.
RAG has a two-stage architecture. In the first stage, it retrieves relevant information from the external knowledge source based on the input prompt. In the second stage, it uses this retrieved information along with the original prompt to generate a response.
GPT excels at creative tasks like generating different text formats, translating languages, and writing different kinds of creative content. It can also be used for question answering, but its accuracy might be limited to the information contained in its training data.
RAG excels at providing factually accurate and contextually relevant responses along with the creative tasks. It's particularly useful for tasks like question answering, where reliable information retrieval is crucial.
GPT can be prone to generating factually incorrect or misleading information, especially when dealing with open-ended prompts or topics outside its training data.
The quality of its responses depends heavily on the quality and relevance of the external knowledge source.
Imagine GPT as a talented storyteller who only relies on their imagination and memory. They can weave captivating tales, but their stories may not always be accurate or true to reality.
RAG is like a storyteller with access to a vast library of books. They can still use their imagination, but they can also draw information and facts from the books, making their stories more grounded and reliable.
Traditional approaches to work with LLM rely primarily on large, pre-trained models and publicly available datasets, with limited options for fine-tuning and customization.
Moreover, maintaining these models would require periodic manual updates and retraining based on new pre-trained models released by developers. This leads to high cost, resource-intensive processes and time-consuming updates, to keep the model updated.
Alternatively, a new approach is to leverage LLMs that can learn and adapt continuously from new data and experiences. This eliminates the need for frequent retraining the entire model, making it more relevant and up-to-date.
Continuous learning and adaptation techniques, such as active learning, transfer learning, meta-learning, and real-time data integration, allow the LLM to continuously adjust its internal parameters based on new information. This improves the model's performance over time.
The knowledge base of the LLM is constantly evolving, as new data is added and user interactions occur, the RAG based system updates its understanding and refines its reasoning capabilities, ensuring that relevance and accuracy are maintained at the forefront.
RAG address several common problems faced with traditional AI implementations like Factual errors, Lack of context and relevance, Black box nature & lack of adaptability to evolving knowledge. Before implementing RAG, organizations should take into account the following considerations.
Organizations are drowning in data, with valuable insights buried under mounds of reports and customer interactions. Traditional AI struggles to navigate this complexity, often generating generic responses or factually inaccurate content. This leads to lost efficiency, frustrated customers, and missed opportunities.
Organisations cant go & simply pick LLM’s out there and expect it to work wonders, LLMs struggle to understand the nuances of organizational contexts & often operate like black boxes, making it difficult to understand how they arrive at their outputs. This lack of transparency can hinder trust and limit the adoption of AI solutions within organizations, & also can be computationally expensive to run and require specialized expertise for maintenance.
Thats where Fluid AI comes in, we are first company to bring the power of LLM’s to the organisation with additional Enterprise required capabilities, easy to use interface, ensuring security & privacy of data. Organisations now dont need to worry about hiring new tech/dev workforce, investing months & years to do complex training, & struggle to keep up the latest tech.
Fluid AI's Copilot brings the power of explainable AI (XAI), shedding light on how RAG arrives at its outputs. Forget months of customization, training or complex coding. Fluid AI makes RAG readily accessible and easy to use, even without a dedicated AI team & ensures continuous updation of technology so your Copilot always operates at the cutting edge.
Book a free demo call with us today and explore the possibilities to unlock the full potential of AI for your organization !
Decision points | Open-Source LLM | Close-Source LLM |
---|---|---|
Accessibility | The 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. |
Customization | LLMs 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 & Development | Benefit 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. |
Support | Support 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. |
Cost | Generally 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 & Bias | Greater 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. |
IP | Code 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 |
Security | Training data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the community | The codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment |
Scalability | Users might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resources | Companies often have access to significant resources for training and scaling their models and can be offered as cloud-based services |
Deployment & Integration Complexity | Offers greater flexibility for customization and integration into specific workflows but often requires more technical knowledge | Typically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor. |
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