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4 Major Applications of Retrieval Augmented Generation to Use Today 4 Major Applications of Retrieval Augmented Generation to Use Today
Retrieval Augmented Generation or RAG for short is a state-of-the-art natural language generation technique that can combine retrieval-based methods with generative... 4 Major Applications of Retrieval Augmented Generation to Use Today

Retrieval Augmented Generation or RAG for short is a state-of-the-art natural language generation technique that can combine retrieval-based methods with generative models to produce high-quality, informative text. Currently, it’s all the rage due to the quality of content being produced. But does it mean that RAG is the end all be all? To answer this, we’re going to take a journey together and in this blog post, we’ll explore the various scenarios where RAG is particularly well-suited, including tasks requiring factual correctness, mitigating hallucinations, domain-specific applications, and transparency and explainability.

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Tasks Requiring Factual Correctness

As AI begins to integrate deeply into certain industries, these domains also have to worry about specific issues such as compliance, liability, and other issues that are directly connected to generated content being factually correct.  So how does RAG make the difference? Well the thing is, that RAG excels in generating text that is factually accurate and consistent with the provided context. 

This makes it an ideal choice for tasks such as:

News article summarization – The world is busy and there are lots of things happening every day. AI needs to be able to help summarize current and ongoing events to a high level or else those who depend on the information would be basing decisions on inaccurate information. 

Scientific report generation – As AI has entered the mainstream, academics, and researchers have been singing its praises for quite some time. In one specific method, it’s making life easier, and that’s in generating scientific reports. After proper checks and balances, this allows researchers to focus more on other pieces of their research while providing summaries for outside colleagues. 

Medical information summarization – For patients with complex medical histories, or even those that are incomplete. These applications allow doctors and specialists to better navigate the flow of patients with up-to-date information to maximize care. 

Legal document summarization – Much like scientific reporting, this field deals with complex and long documents that often can be misunderstood by human evaluators. With RAG, legal documents that can often cite hundreds of laws, case precedents, and more can be reduced to an understandable chunk. 

Financial report generation – The financial world is one of the most documented fields in existence. Because of this vast volume of text data, RAG applications are well suited to sift through the ocean of data to produce concise and accurate reports in the fraction of a fraction of the time of a human. Freeing analysis to work on other projects, and tasks that are better suited for their skills.

 

Mitigating Hallucinations

Hallucinations are a common problem in NLG models, where the model generates text that is not supported by the input data. RAG can help mitigate hallucinations by grounding the generation process in real-world evidence. This is achieved by retrieving relevant documents from a large corpus and using them to inform the generation process. As time goes on, the model’s chances of producing hallucinations begin to fall at a tremendous rate.

Domain-Specific Applications

RAG can be fine-tuned to specific domains, making it capable of generating text that is tailored to the specific requirements of the domain. Thanks to the popularity of LLMs such as ChatGPT, Llama, Gemini, and others, multiple industries are looking for ways to harness the power of AI that can be specific to their particular field. With RAG, these models can be fine-tuned in a manner that allows to them essentially specialize in a particular field. Allowing them to not only generate high-quality text but also reduce the risk of hallucinations when pressed on their domain subjects. 

Transparency and Explainability

Believe it or not, but due to how it works, RAG is a relatively transparent and explainable NLG model. This means that it is possible to understand how the model generates text and to identify the sources of the information it includes. This makes it easier to debug the model and to ensure that it is generating accurate and reliable text, which is often different than how many models currently work.

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Conclusion

As you can see, RAG is a powerful and versatile NLG technique that can be used to generate high-quality, informative text in a variety of scenarios. Its ability to mitigate hallucinations, its transparency and explainability, and its suitability for domain-specific applications make it a valuable tool for a wide range of tasks.  But if you want to learn the latest on RAG, then you’ll want to be where those who are utilizing RAG meet up, and that ODSC East. 

At ODSC East, there’s an entire track solely dedicated to large language models. Learn from the movers and shakers, researchers, and those at the cutting edge of AI. Confirmed sessions include:

  • Enabling Complex Reasoning and Action with ReAct, LLMs, and LangChain
  • Ben Needs a Friend – An intro to building Large Language Model applications
  • Data Synthesis, Augmentation, and NLP Insights with LLMs
  • Building Using Llama 2
  • Quick Start Guide to Large Language Models
  • Build Conversational AI and Integrate into Product Page Using Watsonx Assistant
  • LLM Best Practises: Training, Fine-Tuning and Cutting Edge Tricks from Research
  • Machine Learning using PySpark for Text Data Analysis
  • Large Language Models as Building Blocks
  • Model Evaluation in LLM-enhanced Products
  • LLMs Meet Google Cloud: A New Frontier in Big Data Analytics
  • Operationalizing Local LLMs Responsibly for MLOps
  • LangChain on Kubernetes: Cloud-Native LLM Deployment Made Easy & Efficient
  • Training an OpenAI Quality Text Embedding Model from Scratch
  • Tracing In LLM Applications
  • Moving Beyond Statistical Parrots – Large Language Models and their Tooling
  • Reasoning in Large Language Models
  • Data Automation with LLM
  • CodeLlama: Open Foundation Models for Code
  • RAG, the bad parts (and the good!): building a deeper understanding of this hot LLM paradigm’s weaknesses, strengths, and limitations
  • Prompt Engineering: From Few Shot to Chain of Thought
  • Setting Up Text Processing Models for Success: Formal Representations versus Large Language Models
  • Accelerating the LLM Lifecycle on the Cloud
  • Practical Challenges in LLM Evaluation
  • Deep Reinforcement Learning in the Real World: From Chip Design to LLMs
  • Mastering Langchain for LLM Application Development
  • Applying Responsible Generative AI in Healthcare
  • Power of Fine-tuning Large Language Models (Execution, Best Practices and Tools and Case Study from Microsoft)
ODSC Team

ODSC Team

ODSC gathers the attendees, presenters, and companies that are shaping the present and future of data science and AI. ODSC hosts one of the largest gatherings of professional data scientists with major conferences in USA, Europe, and Asia.

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