In this blog post about Microsoft Build 2024, I will focus on AI Search and RAG modelling. The Microsoft Build Conference, which will take place from May 21 to 23, 2024, is a platform for Microsoft to unveil a range of innovations and announcements. These developments aim to revolutionize efficiency, elevate customer experiences, and foster groundbreaking innovations in various industries. Together, the progress seeks to empower developers, organizations, and individuals using Microsoft technologies, reflecting the company’s commitment to leading the way in the AI revolution. So, let’s dive in!
Azure AI Services have a new emphasis on architectures
Microsoft’s announcements included significant updates within the Azure AI Services; introducing Azure Patterns and Practices for private chatbots marks a considerable leap forward. These reference architectures and implementation guides allow enterprises to deploy intelligent applications confidently, ensuring reliability, cost-efficiency, and compliance.
Furthermore, the anticipated launch of the Custom Generative Mode in Azure AI highlights Microsoft’s innovative approach to document processing. This feature promises to streamline workflows by enabling users to handle complex documents with minimal labelling, embodying the AI’s flexibility and adaptability to various formats and templates. It is sound to align across Microsoft technologies with a focus on architecture. It is better to have an architecture in place that you can tweak moving forward than to build organically.
Solving AI-angst with AI-generated content
Have you ever used ChatGPT or a similar model, and it has come up with a response that is bizarre? These models are part of a paradigm called Artificial Intelligence-Generated Content (AIGC), where we use AI to generate content. Briefly, examples of hallucinations include plausible-sounding answers that are completely incorrect or entirely made up, creating fictitious names, books, or legal citations; or extrapolating from trends to produce false statements. You have probably come across situations where AI has a hallucination, and if you want to know more, you can check out Bernard Marr’s post here. To be entirely fair to AI, humans make errors of judgement all the time, so we can’t expect AI to be altogether error-free, either!
AIGC models could be better; at the time of writing, ChatGPT was up to date until May 2023, one year ago. Overall, AIGC still faces hurdles such as updating knowledge, and retrieval-augmented generation (RAG) has recently emerged as a paradigm to address such challenges of AIGC models. RAG’s objective is to help improve the generative AI model’s accuracy and contextual relevance. RAG achieves this objective by combining generative AI models and integrating them with retrieval-based systems.
What is Retrieval Augmented Generation - RAG?
We’ve had a look at the problem that RAG is trying to solve. You may have heard the term Retrieval-augmented generation (RAG) pop up repeatedly, so let’s look at what RAG is. Retrieval-augmented generation (RAG) AI models represent a significant advancement for businesses. RAG merges the strengths of generative AI with retrieval-based systems to enhance the precision and relevance of generated outputs. By integrating external knowledge sources into generative processes, RAG models provide more accurate and contextually aware responses. This innovation particularly benefits customer service, knowledge management, and content generation businesses.
Overall, the adoption of RAG models can lead to improved customer engagement, streamlined operations, and more effective content creation, providing businesses with a competitive edge in a rapidly evolving technological landscape.
How does Retrieval Augmented Generation (RAG) work?
Firstly, using a retrieval model, RAG retrieves the appropriate information from a large corpus. Then, it conditions a generative model on this information to produce more knowledgeable and precise outputs. This hybrid approach accesses the extensive knowledge embedded in external databases or documents to augment the generative model’s responses.
Azure AI Search with RAG
Another important feature is the enhancements in Azure AI Search, which improves the information retrieval system by integrating retrieval-augmented generation (RAG) and enterprise search capabilities. For developers and AI professionals, Azure AI Search will transform how users interact with large datasets, delivering efficiency and relevance in search results.
RAG use case example
Let’s take a look at a use case. One example is the development of intelligent customer support systems. For instance, a RAG-based chatbot can dynamically fetch the latest product documentation and customer history to provide tailored and up-to-date responses. From the user’s perspective, they get a better, faster more accurate efficient response. The RAG-based chatbot accesses the RAG model, which returns responss to the user by fusing retrieval and generation. It serves to enrich the chatbot’s output while fact-checking to ensure the generated content is grounded in factual and current data.
In customer service, RAG-powered chatbots and virtual assistants can dynamically access up-to-date information from extensive databases, delivering personalized and accurate responses to customer queries, thereby improving satisfaction and operational efficiency. For knowledge management, these models enable the efficient retrieval of specific data from vast corporate knowledge bases, facilitating better decision-making and problem-solving. In content generation, RAG models can produce high-quality, relevant content using up-to-date external sources to support different objectives, such as marketing and communication.
What are the risks of RAG for businesses?
While retrieval augmented generation (RAG) AI models offer significant benefits, businesses must also consider risks and challenges for companies.
Data Privacy in AI
One principal concern is the potential for data privacy breaches, affecting all data-based business endeavours. Since RAG models access external databases, there’s a risk of inadvertently retrieving and revealing sensitive or proprietary information. One primary business objective is to implement and maintain robust data security measures and compliance with privacy regulations is crucial to mitigate this risk.
Garbage in, Garbage Out
Another inherent risk involves the accuracy and reliability of the retrieved information. The generative output may be flawed if the external sources are outdated, biased, or inaccurate. Since this risk leads to misinformation and reputational damage, businesses must implement stringent validation processes to ensure the quality of the data. As with all new shiny technologies, there’s a risk of over-reliance on AI. AI must be human-centred, and ensuring that humans are in the loop is often necessary. AI-generated insights without sufficient human oversight might unduly influence critical business decisions.
AI Architecture and Maintenance
Furthermore, the integration of RAG models can introduce complexities in system architecture and maintenance. Dependency on external data sources requires continuous monitoring and updates, which can increase operational costs and create technical challenges.
Environmental and Financial Costs
Cloud computing may also have environmental challenges, and we know that managing high training and inference costs is costly. I will explore this issue in a later blog post, as I have concerns about the environment and the cost of cloud computing. We keep designing data centres in a 20th-century fashion, but we need 21st-century solutions and beyond for AI and tomorrow’s technology.
Ethical Considerations for Generative AI
Lastly, organizations must address ethical considerations regarding transparency and accountability in AI decision-making processes. Businesses must take care that their use of RAG models aligns with ethical standards and promotes trust among stakeholders.
Rise of RAG models: Moving Forward
As you might expect, AI growth has some downsides; the ethics and concerns of generative AI are only one area. While RAG AI models can significantly enhance business capabilities, addressing data privacy, information accuracy, system complexity, and ethical issues is essential to mitigate associated risks effectively. To summarise, the RAG technique is an invaluable tool in applications requiring high reliability and specificity. As we look forward to the next horizon, benefits and challenges in AI, one thing is clear: the journey has just begun, and it is becoming a tangible reality that organizations are building together, now.
If you’d like to learn more about an actionable way forward, get in touch for a chat.
