Unlocking the Power of Retrieval Augmented Generation (RAG) for Business

EPISODE 205

Hanh Brown hosts this podcast episode about Retrieval Augmented Generation (RAG) and its potential to help businesses in healthcare, legal, real estate, and other data-heavy industries. RAG combines large language models like GPT-4 with specific business data to provide accurate, personalized responses to customer questions. It can help businesses efficiently use their data, saving time and resources. For example, a healthcare chatbot with RAG could give personalized answers about Medicare coverage, and a real estate system could match listings to buyer preferences.

The episode also discusses Azure AI services that can help implement RAG. Hanh encourages listeners to embrace this technology to fully leverage their data and improve efficiency.

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Episode Transcript

EPISODE 205

Hanh: 00:00:05
The most exciting aspect of RAG and Azure machine learning is their potential to democratize AI and make it accessible to businesses of all sizes and industries. With these tools, even small companies and startups can create powerful, intelligent applications that rival those of much larger enterprises.

Narrator: 00:00:30
Welcome to AI50 Connect, your gateway to the extraordinary world of artificial intelligence and its revolutionary impact on businesses, entrepreneurs, and the lives of people across generations. Add AI50. We’re passionate about transforming data into groundbreaking innovations in today’s captivating episode, unlocking the power of retrieval, augmented generation RAG for business. Your host in AI luminary Hanh Brown explores the game changing potential of RAG technology in sectors like

Narrator: 00:01:03
healthcare, legal, real estate and information intensive businesses. Hanh breaks down the intricacies of RAG, revealing how it combines the prowess of large language models with specific business data to deliver highly accurate and personalized responses to customer queries. With AI50’s cutting edge LLM apps, businesses across diverse industries can harness the full potential of RAG, tailoring solutions to their unique needs and preferences. We dive deep into RAG’s ability to revolutionize data handling, streamline

Narrator: 00:01:40
processes, and enhance customer satisfaction in various sectors. Each episode is a thrilling expedition, packed with inspiring stories, trailblazing innovations, and thought provoking conversations with industry leaders. We navigate the advancements and ethical implications of RAG, illuminating how it can drive positive change and fuel business growth. Whether you’re an entrepreneur seeking to leverage RAG for your venture, a business owner aiming to serve clients better, or simply captivated by the

Narrator: 00:02:12
transformative power of AI, join Hanh on this enlightening odyssey. Get ready for captivating storytelling, expert insights, and real world applications that showcase the incredible depth and breadth of RAG. Let’s embark on this exciting journey together.

Hanh: 00:02:34
Hello, everyone, and thank you for joining us on today’s amazing episode, where we’ll be looking at the amazing world of Retrieval Augmented Generation, or REG for short. My name is Hanh Brown, and I’m delighted to be your host on this adventure into the advanced field of data solutions. Picture this, you’re running a healthcare clinic, a legal firm, a real estate agency, or any business that deals with a vast amount of information. You want to provide your clients with the most accurate, personalized, and efficient service possible, but

Hanh: 00:03:08
you’re drowning in a sea of data. That’s where REG comes in. It’s like having a superhero sidekick that can process all your information at lightning speed and deliver the most relevant answers to your client’s questions. I’ll break it down in simple terms and explain how it can be a game changer for businesses, especially in the realms of legal, Medicare, healthcare, and real estate. So what exactly is REG? In a nutshell, it’s a way to combine the power of large language models.

Hanh: 00:03:42
Like GPT 4, with your company’s own specific data. This unique combination allows you to generate highly accurate and relevant responses to customer queries, all while saving time and money on model fine tuning. Imagine you’re running a healthcare clinic and you want to create a chatbot that can help patients understand their Medicare coverage. With RAG, you can feed your chatbot all of the relevant information about Medicare plans, benefits, and eligibility requirements.

Hanh: 00:04:13
Then, when a patient asks a question like, does my Medicare plan cover prescription drugs? The chat bot can quickly scan through the data and provide a personalized answer based on the patient’s specific plan. But that’s not all. RAG can also be a game changer for real estate businesses. Let’s say you have a database of property listings, complete with details about each home’s features and features. Location, and price. With REG, you can create a recommendation engine that suggests properties

Hanh: 00:04:47
to potential buyers based on their preferences and search history. So if a buyer is looking for a three bedroom home with a backyard in a specific neighborhood, your REG powered system can quickly find the best matches and present them to the buyer. The possibilities are endless, whether you’re a legal firm looking to streamline your document search process, or Or a healthcare provider wanting to create personalized treatment plans for patients. RAG can help you unlock the full potential of your data. Now, you might be thinking, this

Hanh: 00:05:21
sounds great, but how do I actually implement RAG in my business? In this episode, we’ll explore the key components of RAC and how you can get started with Azure AI services like Azure AI Search and Azure Machine Learning. Let’s start by talking about Azure AI Search, which is the foundation of RAC. In simple terms, Azure AI Search is a service that allows you to index and search. Through large volumes of data, making it easy to find the information you need when you need it. Think of it like a super powered

Hanh: 00:06:00
search engine for your business data, whether you have text documents, images, or even videos. As your AI search can help you organize and search through it all effortlessly. To put this into context, let’s consider a real world example in the healthcare industry. Imagine you run a large hospital system with multiple facilities, each with its own electronic health record, EHR system. These EHR systems contain a wealth of data about your patients, including their medical histories, medications, allergies, and test results.

Hanh: 00:06:41
But with so much data spread across different systems and formats. It can be a challenge for clinicians to find the information they need quickly and easily. This is where Azure AI Search comes in. With Azure AI Search, you can create a unified index of all your EHR data. Making it easy for clinicians to search and find the information they need, regardless of which facility or system it comes from. For example, let’s say a doctor needs to find all the patients who have been prescribed a certain

Hanh: 00:07:20
medication in the last year. With Azure AI Search, the doctor can simply type in the name of the medication and get instant results showing all of the relevant patient records across the entire hospital system. But Azure AI Search isn’t just about keyword searches. It also uses advanced natural language processing. and machine learning algorithms to understand the meaning and context behind your data. This means that it can provide more accurate and relevant search

Hanh: 00:07:56
results, even when the search query isn’t an exact match for the data. For example, let’s say a doctor searches for patients with high blood pressure, but some of the patient records use the term hypertension instead. With Azure AI Search, the system can understand that these terms are synonymous and return all of the relevant results. Even if the exact phrase, high blood pressure, doesn’t appear in the data. Another key advantage of Azure AI Search is its ability to handle unstructured data, such as clinical notes and discharge summaries.

Hanh: 00:08:36
These types of data are often in a narrative format, with important information buried in long paragraphs of text. With Azure AI Search, you can use advanced techniques like entity recognition and sentiment analysis to extract key insights and make this unstructured data more searchable and actionable. For example, let’s say you want to find all the patients who have a history of smoking and have been diagnosed with lung cancer. With Azure AI Search, you can create a custom entity recognition model

Hanh: 00:09:13
that looks for mentions of smoking and lung cancer in the clinical notes, and then use this information to generate a list of relevant patient records. This kind of advanced analysis would be impossible with a traditional keyword search. Azure AI Search But with Azure AI Search, it becomes a powerful tool for uncovering insights and improving patient care. Finally, AI Search is designed to be flexible and scalable, so it can grow and adapt as your healthcare organization evolves. Whether you’re starting with a

Hanh: 00:09:46
small pilot project, Or rolling out a system wide search solution. AI Search has the tools and capabilities to meet your needs. And with its tight integration with other Azure services, such as Azure Machine Learning and Azure Cognitive Services, You can create even more powerful and intelligent search experiences that improve clinical decision making and patient outcomes. AI Search is the foundation of RAG because it provides a fast, flexible, and intelligent way to search and analyze your healthcare data.

Hanh: 00:10:24
By making it easy for clinicians to find the information they need when they need it, Azure AI Search can help improve the quality and efficiency of patient care while also reducing the burden on healthcare staff. And when combined with other Azure services and the power of RAG, it becomes an even more potent tool for driving innovation and transformation. Let’s say you’re a legal firm with a vast database of case files and court decisions. With Azure AI Search, you can quickly index all these documents and make them

Hanh: 00:11:01
searchable by keywords, dates, and time. Or even specific legal concepts. So, when a lawyer needs to find a relevant case to support their argument, they can simply type in a few keywords and get instant results, saving them hours of manual searching. But how does this relate to RAG? Well, when you’re generating responses using a large language model, it’s crucial to have access to relevant information from your company’s knowledge base. That’s where Azure AI Search comes in. It indexes your data and provides the language model with the

Hanh: 00:11:40
context it needs to generate accurate and helpful responses. For instance, let’s say you have a Medicare chatbot that needs to answer questions about different health plans. With Azure AI Search, you can index all the details about each plan, including coverage, premiums, and network providers. When a user asks a question like, What’s the best Medicare plan for someone with diabetes? The chatbot can quickly search through the indexed data and provide a personalized recommendation based on the user’s specific needs.

Hanh: 00:12:19
This is a game changer for businesses because it means you can provide fast, accurate support without having to manually search through countless documents yourself. Plus, as your knowledge base grows and evolves, Azure AI Search can easily keep up, ensuring that your RAG system always has access to the latest information. So, to recap, Azure AI Search is the foundation of RAG because it enables you to index and search through your company’s data, providing the language model with the context it needs to generate relevant and accurate responses.

Hanh: 00:12:59
In the next section, we’ll explore how you can use Azure Machine Learning to actually implement REG in your business. Now that we’ve covered the basics of Azure AI Search, let’s talk about how you can actually implement REG using Azure Machine Learning. Think of Azure Machine Learning as a user friendly tool that helps you create smart applications, like chatbots or recommendation systems. Without needing to be a tech wizard. It’s like having a personal assistant that handles all the complex behind the scenes work for you.

Hanh: 00:13:40
To give you a real world example, let’s say you’re a healthcare provider looking to create a personalized treatment plan for a patient with chronic back pain with Azure Machine Learning and RAG. You can create a system that analyzes the patient’s medical history, lifestyle factors, and treatment preferences, and then generates a customized plan that takes all these factors into account. The system can even suggest alternative therapies or medications that Based on the latest research and clinical guidelines, but how does this all work in practice? Let’s walk through the steps of

Hanh: 00:14:17
implementing RAG with as your machine learning using a real world example from the legal industry. Imagine you’re a law firm that specializes in intellectual property. Your firm has a vast database of past cases. Legal briefs and court decisions related to IP law. But it’s a challenge for your lawyers to sift through all this information and find the most relevant precedents and arguments for their current cases. With Azure Machine Learning and RAG, you can create a system that helps

Hanh: 00:14:56
your lawyers find the information they need quickly and easily. Here’s how it works. First, you use Azure AI Search to index all your legal data, including case files, briefs, and court decisions. This creates a unified search index that your lawyers can query. You then use Azure Machine Learning to create a custom RAG model that understands the language and terminology of IP law. This model is trained on your specific legal data, so it can provide highly accurate and relevant results. You then use Prompt Flow to create

Hanh: 00:15:31
a user friendly interface for your lawyers to interact with the RAG model. This could be a simple search box where lawyers can type in their queries, or a more advanced interface that allows them to filter results by case type, jurisdiction, or other criteria. When a lawyer types in a query, such as find cases related to patent infringement in the software industry. The REG model uses Azure AI Search to find the most relevant cases and briefs and then generates a summary of the key arguments and precedents. The lawyer can then review the

Hanh: 00:16:10
summary and decide which cases and arguments to use in their current case. They can also provide feedback on the relevance and usefulness of the results, which is used to continuously improve the RAG model over time. By using this RAG system, your law firm can save countless hours of manual research and help your lawyers build stronger, more persuasive cases. And because the system is built on Azure machine learning, it can scale to handle even the largest and most complex legal databases without sacrificing speed or accuracy.

Hanh: 00:16:51
But the benefits of RAG and Azure machine learning don’t stop there. Because these tools are so flexible and customizable, you can use them to create all kinds of intelligent applications across a wide range of industries and use cases. for listening. For example, in the real estate industry, you could use RAID to create a system that helps property managers find the most relevant maintenance records and vendor contracts for their buildings. In the retail industry, you could use RAG to create a system that helps

Hanh: 00:17:22
store managers forecast demand and optimize inventory levels based on past sales data and customer behavior. The possibilities are endless, and the best part is that you don’t need to be a machine learning expert to get started. With Azure Machine Learning and PromptFlow, you can create powerful RAG systems using a simple, intuitive interface that guides you through the process step by step. Of course, as with any AI system, there are some best practices and considerations to keep in mind when implementing RAG with Azure Machine Learning.

Hanh: 00:18:00
For example, you’ll want to make sure that you have a clear understanding of your data. And the specific problem you’re trying to solve. You’ll also want to invest time in testing and refining your RAG model to ensure that it’s providing accurate and relevant results, but with the right approach and the power of Azure machine learning and RAG, you can create intelligent applications that transform the way you work and serve your customers. Whether you’re. In healthcare, legal, real estate,

Hanh: 00:18:32
or any other industry, RAG and Azure Machine Learning can help you unlock the full potential of your data and drive innovation like never before. Another example, let’s say you’re a healthcare provider looking to create a personalized treatment plan for a patient with chronic back pain. With Azure Machine Learning and RAC, you can create a system that analyzes the patient’s medical history, lifestyle factors, and treatment preferences, and then generates a customized plan that takes all these factors into account. Azure Machine Learning The system can

Hanh: 00:19:11
even suggest alternative therapies or medications based on the latest research and clinical guidelines. One of the coolest features of Azure Machine Learning is called Prompt Flow, which makes implementing RAG a breeze. Imagine you have a bunch of data scattered across different files and folders, like patient records, medical journals, and more. or clinical trial results. With PromptFlow, you can easily organize and combine this data with a powerful language model, creating a RAG system that can understand and respond to

Hanh: 00:19:55
complex medical queries in no time. But how does this all work under the hood? Let’s dive a bit deeper into the technical concepts behind RAG and Azure Machine Learning. At its core, RAG is all about using machine learning algorithms to generate relevant responses based on a given input or query. The key components of a RAG system are the language model, which is trained on a large corpus of text data, and the retrieval mechanism, which fetches relevant information from your company’s knowledge base.

Hanh: 00:20:29
Azure Machine Learning provides a range of pre built models and tools that make it easy to implement these components without needing to start from scratch. For more technically inclined listeners, it’s worth noting that Azure Machine Learning supports a wide range of machine learning frameworks and libraries, including popular ones like TensorFlow, PyTorch, and Sikkit Learn. This allows developers to leverage existing models and architectures, or build custom models from scratch. Azure Machine Learning also provides a suite of tools for data

Hanh: 00:21:06
preprocessing, feature engineering, and model evaluation, enabling end to end machine learning workflows. For example, you can use the prompt flow feature to create a pipeline that takes your input data, processes it using various machine learning algorithms, and then generates an output based on the retrieved information under the hood. PromptFlow uses techniques like data encoding, semantic search, and natural language processing to understand the meaning and context behind your data. It then uses this understanding to generate a relevant response

Hanh: 00:21:46
that takes into account it. Thank you. For those interested in the technical details, PromptFlow leverages state of the art deep learning models, like transformers, to encode data into dense vector representations. These representations capture the semantic meaning of the data, allowing for more accurate retrieval and generation. PromptFlow also employs advanced techniques like cross attention and self attention to weigh the importance of different data points and generate more coherent and

Hanh: 00:22:19
contextually relevant responses. But the benefits don’t stop there. PromptFlow also makes it easy to keep your RAG system up to date as your business evolves. Let’s say you add new products to your store or receive fresh customer reviews. With PromptFlow, you can easily retrain your language model and update your data, ensuring that your chatbot always has the latest information at its fingertips. This is a game changer for businesses of all sizes. Instead of spending countless hours and resources building a RAG system

Hanh: 00:22:59
from scratch, You can use PromptFlow to get up and running quickly. It’s like having a ready made solution that you can customize to fit your specific needs. Plus, as you collect more data and feedback from your customers, PromptFlow makes it easy to continuously improve your RAC system. It’s like having a smart assistant that learns and grows with your business, helping you stay ahead of the curve and provide better service to your customers. PromptFlow. com So, whether you’re a small online

Hanh: 00:23:28
store looking to create a simple chatbot, or a large enterprise wanting to build a sophisticated recommendation engine, Azure Machine Learning and PromptFlow have you covered. With these tools at your fingertips, you can create intelligent applications. That understand your customers needs and provide personalized experiences all without breaking a sweat. While RAG and PromptFlow offer many benefits for businesses, implementing these technologies can come with its own set of challenges. One of the biggest hurdles is

Hanh: 00:24:12
dealing with unstructured and siloed data that’s scattered across different systems and formats. Let’s say you’re a real estate company with a database of property listings. But the information is spread out across multiple spreadsheets, documents, and images. Before you can use RAG to create a recommendation engine, you first need to consolidate and organize all this data in a way that’s easy to search and analyze. This is where Azure AI Search and Prompt Flow come in handy. With these tools, you can easily

Hanh: 00:24:50
ingest and index your data from various sources, creating a unified knowledge base that’s ready for RAG. Diving deeper into the technical aspects, Azure AI Search uses advanced indexing techniques like inverted indexes, and Term Frequency Inverse Document Frequency, TF IDF, to efficiently store and retrieve data. It also supports custom analyzers and tokenizers, allowing you to tailor the indexing process to your specific data and use case. PromptFlow, on the other hand, provides a declarative language for defining

Hanh: 00:25:33
data ingestion and processing pipelines. Making it easy to build complex data workflows without writing low level code. PromptFlow even offers pre built connectors and APIs. That make it easy to integrate with popular data sources like Salesforce, SharePoint, and Dynamics 365. Another challenge businesses face is keeping their RAG system up to date with the latest information in industries like healthcare and legal, new regulations, guidelines. and best practices are constantly emerging.

Hanh: 00:26:11
And it’s crucial to ensure that your REG system is always providing accurate and compliant information. With PromptFlow, you can easily retrain your language model and update your data on a regular basis, ensuring that your REG system stays current and reliable. You can even set up automated workflows that trigger retraining and updating based on specific events or schedules. Thanks. So you don’t have to worry about falling behind. But perhaps the biggest challenge businesses face is simply knowing

Hanh: 00:26:42
where to start with RAG and PromptFlow. Implementing these technologies can seem daunting, especially if you don’t have a background in data science or machine learning. That’s where the ease of use and accessibility of Azure Machine Learning and PromptFlow come in. These tools are designed to be user friendly and intuitive, even for non technical users. www. microsoft. com With guided workflows, drag and drop interfaces, and pre built templates, you

Hanh: 00:27:11
can create powerful RAG systems without needing to write a single line of code. Plus, Azure Machine Learning and PromptFlow come with extensive documentation, tutorials, and support resources to help you get started and troubleshoot any issues along the way. Whether you’re a beginner or an experienced developer, Azure You’ll find plenty of guidance and best practices to help you succeed with RAG. As RAG and Azure machine learning continue to evolve, the possibilities for businesses are truly endless. In the coming years, we can expect to

Hanh: 00:27:51
see even more advanced and sophisticated applications of these technologies across a wide range of industries and use cases. One exciting area of development is in the realm of personalized medicine. With RAG and Azure Machine Learning, healthcare providers could create systems that generate customized treatment plans based on a patient’s unique genetic profile, medical history, and lifestyle factors. This could revolutionize the way we approach diseases like cancer, diabetes, and heart disease, leading to more targeted and effective therapies.

Hanh: 00:28:32
Another promising application of RAG is in the field of predictive maintenance. By analyzing vast amounts of sensor data from machines and equipment, RAG systems could predict when a particular component is likely to fail, allowing businesses to schedule maintenance and repairs proactively. This could help companies avoid costly downtime and improve overall efficiency and reliability. But perhaps the most exciting aspect of RAG and Azure machine learning is their potential to democratize AI and make it accessible to businesses

Hanh: 00:29:06
of all sizes and industries. With these tools, even small companies and startups can create powerful, intelligent applications that rival those of much larger enterprises. As the technology continues to mature and become more user friendly, we can expect to see a proliferation of RAG powered chatbots, recommendation engines, and decision support systems across a wide range of domains. From customer service and sales to healthcare and finance, RAG has the potential to transform the way businesses operate and interact with their customers.

Hanh: 00:29:43
Of course, with great power comes great responsibility, and it’s important for businesses to use RAG and Azure machine learning in an ethical and transparent manner. This means being clear about how data is being collected and used. Providing users with control over their personal information and ensuring that the outputs of RAG systems are fair, unbiased, and aligned with human values. But with the right approach and safeguards in place, the future of RAG and Azure machine learning is truly bright. As these technologies continue to

Hanh: 00:30:18
evolve and mature, they have the potential to unlock new insights. Drive innovation and create value for businesses and society as a whole. And that’s an exciting prospect indeed. In today’s episode, we’ve taken a deep dive into the world of retrieval augmented generation or REG, a powerful approach that combines the best of large language models and custom business data to create intelligent applications that can transform industries. We started by exploring the basics of RAG and how it works. We saw how RAG can help businesses provide

Hanh: 00:30:59
personalized and accurate responses to customer queries, while also saving time and money on model fine tuning. Next, we looked at Azure AI Search. The foundation of REG and how it provides a fast, flexible, and intelligent way to search and analyze large volumes of data. We explored how Azure AI Search can handle structured and unstructured data, and how it uses advanced techniques like natural language processing and entity recognition to provide more accurate and relevant search results. We then turned our attention to Azure Machine Learning, and And how it

Hanh: 00:31:43
provides a user friendly platform for building and deploying RAG models. We walk through a step by step example of how to implement RAG using Azure machine learning and prompt flow. And how this can help businesses in industries like legal, real estate and retail unlock the full potential of their data. Throughout the episode, we emphasize the importance of taking a thoughtful and strategic approach to implementing RAG. And how businesses need to have a clear understanding of their data and the problems they’re trying to solve.

Hanh: 00:32:20
We also highlighted the benefits of RAG, including improved accuracy, efficiency, and customer satisfaction, and how it can help businesses stay ahead of the curve in an ever changing landscape. As we wrap up today’s episode, I want to leave you with a few key takeaways. First, RAG is a powerful approach that can help businesses of all sizes and industries create intelligent applications. That drive innovation and growth. Second, as your AI search and as your machine learning provide a comprehensive and user friendly

Hanh: 00:33:03
platform for implementing RAG, even if you’re not a machine learning expert. And third, by taking a thoughtful and strategic approach to RAG businesses can unlock the full potential of their data and create intelligent applications that transform the way they work and And serve their customers. If you’re interested in learning more about RAG and how it can benefit your business, I encourage you to reach out to our team. We can walk you through this process. And if you enjoyed today’s episode, I would love to hear

Hanh: 00:33:41
your thoughts and feedback. Please take a moment to leave a review and rating on your favorite podcast platform, and let us know what you found most valuable and interesting about today’s discussion. Your feedback helps us continue to create content that is relevant, informative, and engaging for our listeners. Thank you for tuning into today’s episode of the AI in Business podcast. Until next time, stay curious and keep exploring the exciting world of AI and machine learning.

Narrator: 00:34:17
Wow. What a mind expanding exploration of retrieval, augmented generations, potential for businesses and entrepreneurs. Hanh hopes this episode of AI50 Connect has ignited your imagination and inspired you to consider how RAG can help you scale your venture and deliver unparalleled service to your clients. Remember, RAG is more than just a fancy acronym. It’s a transformative tool that empowers you to make data driven decisions, uncover hidden opportunities, and

Narrator: 00:34:45
revolutionize your business processes. With AI50 as your trusted partner, you can fully leverage the power of RAG to propel your business to new heights. Visit our website, AI50.ai, to discover more about our cutting edge RAG solutions tailored for businesses of all sizes. While you’re there, be sure to subscribe to our newsletter and stay at the forefront of the latest AI trends, insights, and success stories. Don’t forget to hit that subscribe button on AI50. Connect on your favorite podcast platform and leave us a review.

Narrator: 00:35:21
Your feedback is invaluable in helping us create content that addresses your most pressing AI questions and concerns. Connect with us on LinkedIn, TikTok, Twitter, and YouTube for a treasure trove of RAG related content designed to keep you ahead of the curve. And when you’re ready to embark on your RAG journey, reach out to us at AI50.ai, our team of experts is here to guide you every step of the way. Thank you for joining Hanh on this mind expanding RAG adventure. Remember with AI50, you’re not just keeping up with

Narrator: 00:35:52
change, you’re pioneering it. Until next time, keep innovating, growing, and harnessing the power of RAG to transform your business and the lives of your clients.

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