Generative AI
Generative AI (GenAI) is a branch of AI that can be trained to create derivative content in different formats, including text, image, video, and audio.
Why is Generative AI Important?
Generative AI is important to business because it helps accelerate creative processes, including writing copy and sourcing images for ads, customer emails, and newsletters. Product designers benefit from using GenAI to deliver 3D images and models of designs from new perspectives.
Consumers benefit from GenAI by having search results explained to them.
Applications of Generative AI
New applications for GenAI are released almost daily. Below are some examples of this rapidly evolving set of applications:
- Chatbots are probably the most popular text-based application of Generative AI. Customer service teams use these sales contact centers and marketing websites to provide highly responsive dialogs.
- Transcription GenAI services will create meeting minutes and summarize video content.
- Social media analysis GenAI models analyze social streams to get the gist and highlight particularly negative or positive sentiments.
- Research can be more productive by having a GenAI tool run web searches for articles and papers and then summarize and organize the output based on search terms.
- Marketing teams can use GenAI to create visual and written content.
Training Generative AI Models
Generative Pre-trained Transformer (GPT) models use deep learning algorithms applied to large training data sets to accumulate knowledge. Below are the training methods.
Unsupervised Training
The least sophisticated training approach is to feed large volumes of relevant data to teach the GenAI model. For example, you may want a text-based GenAI to write your PR agency’s first draft of press releases. You could start by sharing client briefing templates along with the final draft of the associated press release. The GenAI model will quickly learn to draft similar releases.
Supervised Training
A more guided approach uses data sets with the best usage examples highlighted or tagged. This has the potential to create higher grade output than the unsupervised approach.
Reinforcement Learning from Human Feedback
Reinforcement Learning from Human Feedback (RLHF) provides feedback on GenAI output using people’s preferences—this form of fine-tuning training results in more natural conversational responses from Chatbot applications. For an application that summarizes articles, for example, any edits made to its output are used to generate a further training dataset for fine-tuning.
Diffusion Models
Diffusion models are used for GenAI applications that create and enhance images and videos. In this instance, image creation is done using text-based prompts to provide information about the required frame, subject and style. GPT image tools such as Dall-E2 and Microsoft Designer use diffusion models to create versions of the images they are trained on that depict new perspectives, change settings and allow customizations such as adding text.
Pre-Built Models
GenAI vendors like AWS and OpenAI Enterprise customers can access plug-ins that provide a pre-trained model as a high-level starting point. Below are some examples of GPT-4.
- AI Data Analyst – Explore data using natural language.
- AnalyticsAI – Review your Google Analytics using prompts.
- Bramework – Analyzes search data to help marketers with Search Engine Optimization (SEO).
- Chat With Excel – Converse with your spreadsheet.
- Developer Doc Search – Open-source code research and documentation search.
- Recipe Finder – Recipe ideas organized by dietary needs.
- Rephrase AI – Turn text into talking avatar videos.
- Smart Slides – Create a slide presentation.
- Take Code Captures – Beautify source code for sharing.
- Visualize Your Data – Create charts of your data.
Actian and the Data Intelligence Platform
Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.
Through its centralized interface, Actian supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.
FAQ
Generative AI refers to models that create new content—such as text, images, audio, code, or video—based on patterns learned from training data. These models include transformers, diffusion models, large language models (LLMs), and multimodal systems.
Generative AI models analyze large datasets, learn statistical patterns, and use probability-based generation to produce new outputs. Techniques include transformer-based sequence modeling, diffusion de-noising processes, and autoencoder-style latent representations.
Use cases include chatbots, content creation, summarization, image generation, synthetic data creation, code generation, workflow automation, marketing personalization, and retrieval-augmented generation (RAG) for enterprise knowledge systems.
Challenges include hallucinations, bias, copyright concerns, data privacy risks, difficulty validating generated content, high compute costs, and ensuring model alignment with enterprise governance policies.
Enterprises use generative AI for document automation, customer support, report summarization, design prototyping, predictive insights, knowledge search, and improving productivity in data engineering, analytics, and software development.
Running generative AI typically requires GPUs or specialized accelerators, vector databases, scalable storage, orchestration frameworks, embedding pipelines, monitoring tools, and strong governance to manage data quality, access, and model outputs.
