In the first issue of Follow The Stream GenAI, we invite you to dive into the fundamentals of generative AI and its seamless integration with data streaming. After exploring the Fundamentals section, feel free to scroll through the whole issue or jump to role-specific sections using the navigation links above. Enjoy!
Data Augmentation: Often, the first step in building a GenAI application is developing and populating a vector store for retrieval-augmented generation (RAG). Confluent plays a crucial role in this process by simplifying the integration of disparate data across your enterprise through our comprehensive connector strategy. With over 120 connectors, Confluent facilitates real-time data sourcing and synchronization.
Subtitle 1
Subtitle 2
Subtitle 3
What is Retrieval-Augmented Generation (RAG)? RAG is an architectural pattern in generative AI designed to enhance the accuracy and relevance of responses generated by Large Language Models (LLMs). It works by retrieving external data from a vector database at the time a prompt is issued. This approach helps prevent hallucinations, which are inaccuracies or fabrications that LLMs might produce when they lack sufficient context or information. Consider an AI chatbot using an LLM without RAG. Without context, trustworthiness, or real-time data, LLMs cannot deliver meaningful value to users.
In 2024, generative AI has become widely adopted with nearly two-thirds of organizations integrating it across multiple business functions, a significant increase from recent surveys. Success lies in aligning GenAI applications closely with core business opportunities rather than adopting technology for its own sake. Leading companies are distinguishing themselves by reimagining entire workflows, not just embedding AI into existing processes. Effective implementation requires robust, scalable AI frameworks and constant adaptation, with collaboration across departments crucial for maximizing impact and productivity. “To create value, organizations must have all the elements in place—domain reimagining abilities; relevant skill sets (including the upskilling of nontechnical colleagues); a robust operating model; proprietary data.”
Echoing the old adage "garbage in, garbage out," successful GenAI implementation relies on data quality, thereby introducing complex challenges in data governance:
GenAI consumes both structured and unstructured data at an unprecedented speed and scale
GenAI generates insights unpredictably using a vast repository of data
Security, privacy, and consent require new processes that don’t exist today.
A key mindset shift requires data management to “move from the cleansing and control of discrete data sets into the ongoing, active curation of conversations, both prompt and response.”
No Data = No AI. Data Streaming for Actionable Intelligence with AI
Real-time data is at the heart of successful AI integration. Besides cleaning and preparing data required to train the initial model, businesses have to consider data accessibility on the enterprise-wide level. With each successive AI effort more and more data is required, which leads to increased costs and complexity. However, when data is distributed across the organisation in real time as it is created, AI tools deliver maximum value and drive actionable intelligence. Data streaming gives AI applications:
Findings from Confluent’s 2024 Data Streaming Report reveal that 51% of IT leaders cite data streaming platforms (DSPs) as enabling their organizations to be truly nimble by providing real-time visibility into operations and customer interactions. And 63% say DSPs extensively or significantly fuel AI progress by building the real-time data foundation needed to propel such initiatives. Data streaming platforms have emerged as a key enabler for AI adoption — allowing businesses to tap into continuously enriched, trustworthy, and contextualized data for quickly scaling and building real-time AI applications.
How Global AI Interest is Boosting the Data Management Market
AI models require thorough data management to effectively train and operate within specific business contexts and environments. Companies need to assess the seven essential components of their data management technology stack to tailor models accordingly: Sources, Ingestion, Storage, Transformation, Analytics, Governance and Security, and Orchestration. In Q1 2024, AI was discussed by CEOs in nearly one third of earnings calls, while data management was mentioned in just 1%. This highlights a current oversight in data management strategy, which is crucial for any AI advancements.
Although the use of Big Data is crucial for business success, integrating AI with real-time data streams presents significant challenges, including data governance, infrastructure readiness, and mitigating data biases. Addressing these requires a strategic approach that encompasses robust infrastructure, continuous model monitoring, and adaptive learning techniques to fully harness GenAI's potential.
The Data Streaming Platform: Key to AI Initiatives
Join IDC and Confluent to explore how GenAI is reshaping today’s business landscape, offering real-time context and solutions that cater to your organization's needs. Discover the role of Confluent's data streaming platform in bridging the gap between legacy systems and GenAI use cases. Gain insight into uncertainties that have arisen as the GenAI landscape continues to evolve — and learn how to address them.
A CPO’s perspective – Real Success with GenAI Requires Real-Time Data
To succeed with GenAI applications, avoid treating them as just projects; instead, enable continuous data flow across the organization, use modular data integration, and start with the end goal to guide your approach. A recent report published by analyst firm Gartner recommends that product leaders:
Although AI and machine learning (ML) have become mainstream in today's business world, truly innovative models depend on real-time data streaming for accuracy. “Data streaming is the central nervous system for data, while AI/ML algorithms are the brain.” –Tweet ThisTrusted data is especially crucial for user-facing applications such as automated self-service systems (chatbots), as they can revolutionise CX - answering inquiries faster than ever - or “produce coherent nonsense” resulting in repetitional risk.
Despite the absence of comprehensive AI-specific legal and regulatory requirements in many regions, global organizations should understand how Generative AI operates and the risks associated with this technology. Deloitte recommends that executives consider the following implications:
The ethical considerations and challenges associated with the rapid adoption of GenAI can be summarized in three key concerns: model bias, AI usage, and data responsibility. Data teams are positioned as essential gatekeepers in mitigating these concerns due to their unique understating of data and data integrity. Key strategies for data teams to remain ethical include, securing a seat at the decision-making table, leveraging methodologies like Retrieval Augmented Generation (RAG) to curate responsible data, and prioritizing data reliability through robust data observability. However, “even with a great RAG strategy, your AI is still only as useful as your data is reliable.”
The Most Recognized and Experienced Risks of GenAI
Businesses are increasingly aware of the risks associated with GenAI, particularly regarding data management and operational integrity. Concerns range from data privacy and bias to intellectual property infringement and inaccurate model outputs. Recent surveys indicate a growing recognition among organizations of the impact of inaccuracies and IP issues on GenAI deployments, with a significant number reporting negative consequences such as cybersecurity breaches and lack of explainability.
The AI black box is an issue of transparency and interpretability behind AI systems whose internal processes are invisible to people who develop them. As emphasised by Rachel Aldighieri, managing director of trade body the Data and Marketing Association (DMA), “It’s really important to unpack how AI works: it’s not algorithms that are necessarily causing issues around data privacy and ethics — it’s the data practises companies are using.”
Data Streaming Platforms: Unlocking AI Potential for Enterprise Organizations
GenAI and machine learning (ML) are empowering businesses to deliver the immersive experiences customers demand, while simultaneously streamlining backend operations to new levels of efficiency. However, AI initiatives come with unique data challenges, particularly when data is spread out and siloed, as we are still in the pivotal phase of change and experimentation when it comes to applying AI and ML to enterprise data.
Your AI Data Problems Just Got Easier with Data Streaming for AI
Many organizations struggle with AI implementation due to poor access to clean and trustworthy data. Effective AI models require high-quality, reliable, and fresh data, which is often difficult to achieve with outdated, batch-based data integration methods. Confluent is addressing these challenges by integrating critical AI technologies and committing to product enhancements. Confluent helps enable real-time AI and machine learning use cases including:
The Role of a Data Mesh & Data Products | Generative AI Navigating the complexities of data acquisition for Generative AI models is challenging. However, the combination of data products and data mesh offer innovative solutions. Businesses are leveraging these technologies to enhance generative AI capabilities because they “serve as…much-needed tools in the quest for quality data” and streamline the transformation of raw data into usable formats.
Why, When, and Where to Begin: GenAI for Customer Experience
As demonstrated through practical and measurable use cases, IT architects can utilize GenAI to improve customer experience with advanced self-service systems and personalized interactions, leveraging natural language processing to interpret extensive customer data. The technology also streamlines developer productivity with tools like GitHub Copilot for automated code generation and testing. Additionally, GenAI enhances data science through synthetic data creation for machine learning and strengthens security by detecting phishing and other threats with AI-driven analysis.
Uniting the Machine Learning and Data Streaming Ecosystems
Uniting machine learning (ML) and data streaming requires overcoming both technical and socio-technical barriers. Organizations need to adopt decentralized, domain-driven approaches like data mesh and feature-oriented teams, shifting away from traditional centralized data management. Utilizing Apache Kafka® as the backbone for real-time data integration can operationalize ML by providing the speed and scalability required for real-time analytics. This combined approach enables organizations to leverage real-time ML effectively, driving innovation and responsiveness.
4 Steps for Building Event-Driven GenAI Applications
Building GenAI applications entails developing complex applications spanning many skill sets. The goal isn’t to build a single GenAI-enabled application. For GenAI to truly transform your business, your team will deliver tens or hundreds of specialized applications over time. LLM-driven applications usually have four steps—data augmentation, inference, workflows, and post-processing. Using an event-driven approach for each makes development and operations much more manageable.
Confluent’s AI Assistant is designed to help developers and data scientists interact with its data streaming platform using natural language. By leveraging Confluent documentation and contextual customer data, the assistant can transform natural language queries into precise code and answers. This tool aims to simplify and streamline tasks, troubleshooting, and guidance related to data streaming and management. Analyst’s Take – "These new AI-focused initiatives empower organizations to harness the power of AI more effectively and efficiently, which can lead to improved business outcomes, operational efficiency, and innovation.”
GenAI - Retrieval-Augmented Generation (RAG) and Data Streaming
Is your AI chatbot hallucinating? LLMs are a great foundational tool that has made AI accessible for everyone, but they lack real-time domain-specific data. This is where RAG comes in. RAG is a pattern that pairs prompts with real-time external data to improve LLM responses. Watch this on-demand webinar to learn:
How to build a real-time, contextualized, and trustworthy knowledge base
Where data streaming and Apache Flink® stream processing fit in the RAG architecture
How a RAG demo works, featuring an AI chatbot that provides personalized product recommendations — built using Confluent, OpenAI, ChatGPT-4, Flink, MongoDB, and D-ID
By leveraging event streaming and vector embeddings, GPT-4 can be modified into real-time GenAI support agent that provides precise, context-aware responses. To build a real-world, production application with GPT-4, businesses need to integrate its general capabilities with their unique data:
Event Stream Integration: Merge data from various systems into a unified customer view
Search-Based Prompting: Enhance responses by adding relevant customer data to each prompt
Vector Database Usage: Employ embeddings and a vector database
Plugin Integration and Observability: Integrate ChatGPT plugins for streamlined interactions
Real-Time Machine Learning and Smarter AI with Data Streaming
Are bad customer experiences really just data integration problems? Can real-time data streaming and machine learning be democratized in order to deliver a better customer experience? Airy, an open-source data-streaming platform, uses Apache Kafka® to supply its clients with real-time data and offers integrations with standard business software. In this episode, Airy CEO and co-founder Steffen Hoellinger explains how his company is making data streaming universally accessible and expanding the reach of stream-processing tools and ideas beyond the world of programmers.
Upcoming Webinar: How to Build RAG Using Confluent with Flink AI Model Inference and MongoDB
Thursday, August 29, 2024 10 AM PDT | 1 PM EDT | 10 AM GMT | 10:30 AM IST | 1 PM SGT | 4 PM AEDT Experts Britton LaRoche, Staff Solutions Engineer at Confluent, and Vasanth Kumar, Principal Architect at MongoDB, will walk you through a RAG tutorial using Confluent data streaming platform and MongoDB Atlas. Register to learn:
How to implement RAG in 4 key steps
How to use data streaming, Flink stream processing and AI Model Inference, and semantic vector search
Step-by-step walkthrough of vector embedding for RAG
And get all your questions answered during live Q&A
“Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions. Still, our research indicates the technology could deliver productivity with a value ranging from 10 to 15 percent of overall R&D costs.” Despite some limitations in applicability across industries, GenAI can enhance productivity by quickly producing candidate designs, reducing design costs, and optimising designs for manufacturing to lower costs associated with logistics and production. Other benefits of GenAI in R&D include the potential to develop higher-quality products as well as speed up testing and trial phases.
Generative AI can significantly enhance all stages of the product development process, aligning with the 'V' model used by many companies. From initial discovery and concept design to specification development, supplier interactions, implementation, and regulatory approvals, generative AI supports every phase. For example, Generative AI tools like Synopsys Inc's Synopsys.ai Copilot allows engineers to use complex Electronic Design Automation (EDA) and Computer-Aided Design (CAD) applications through natural-language interfaces, addressing the 30% skills gap in chip design.
Three vectors for Success for GenAI in R&D and Operations
To harness the extensive potential of GenAI in R&D, companies should integrate it into their digitalization strategies, develop scalable tools, and upskill their workforce. GenAI, while promising, requires specific strategies to avoid pitfalls:
Experiment in Sandboxes: Quickly iterate GenAI concepts in isolated environments
Build Strong Data Foundations: Maintain structured data models across enterprise systems
Develop Strategic Partnerships: Evaluate whether to develop GenAI internally or integrate off-the-shelf solutions from providers
