Generative AI Explained: How It Works in 2025

Introduction

Generative AI isn’t just powering futuristic demos or niche tools—it’s now the creative engine behind everything from AI-designed clothes to smart shopping feeds. It’s transforming how we create, shop, communicate, and experience content. From apps like Glance AI, which blend fashion and tech through AI-generated looks, to real-time voice assistants and AI-written emails, this technology is reshaping digital life in India and globally.

So what exactly is Generative AI, and why is it central to the future of digital commerce and content?

Whether you're a curious consumer, a content creator, or a business leader, understanding how generative AI works—and where it’s going—is essential. It helps decode the tech shaping everyday decisions, from what outfits you see on AI shopping apps to how personalized email campaigns are written at scale.

This 2025 guide breaks down:

• How generative AI works (training, models, and outputs)
• Real-world use cases across industries
• Benefits, risks, and ethical concerns
• What’s next in AI commerce and content creation

And most importantly, you’ll see how platforms like Glance AI are building AI-native user experiences—from digital twins to AI avatars in shopping—all powered by generative models.

For a foundational explainer, see What is Generative AI?

What Is Generative AI?

Generative AI is a branch of artificial intelligence that enables machines to create new content—text, images, video, music, and code—by learning from massive datasets. Unlike traditional AI, which focuses on prediction, classification, or decision-making, generative models produce original outputs that mimic human creativity.

This makes it the driving force behind innovations like AI-generated fashion content, chatbot conversations, product mockups, or even hyper-personalized shopping experiences like those built into Glance AI’s platform.

Common Output Types

What Makes It ‘Generative’?

Instead of retrieving pre-written content, generative AI models create new content by learning the patterns, structure, and context of their training data. For example, a model like GPT-4 doesn’t store a script—it learns how language flows, then writes fresh content on demand.

That creative capacity is what powers everything from realistic AI avatars to auto-personalized email campaigns or synthetic product images.

Key Technologies Behind Generative AI

Generative models are typically built using Transformer architecture, introduced by Google Research in 2017, which enables self-attention and context retention across large sequences. This breakthrough architecture fuels the performance of today’s large language models (LLMs) and text-to-image generators.

Key Takeaway

Generative AI isn’t just reshaping content—it’s reinventing how creativity, commerce, and communication operate. It powers everything from try-on features in fashion tech to entire customer journeys in AI shopping ecosystems.

How Generative AI Works: A Technical Breakdown

Generative AI doesn’t just “copy-paste” content—it learns how things work and generates new results from scratch. Here’s how the tech stack behind this innovation functions, from raw data to intelligent creation.

1. Data Collection

AI models require massive datasets to learn from. Language models like GPT-4 are trained on trillions of words pulled from websites, books, research papers, and conversations. Visual models are trained on datasets like ImageNet or LAION-5B, containing billions of image-text pairs.

This data helps the model understand context, grammar, tone, composition, and other patterns. Ethical platforms ensure data is legally sourced and representative.

2. Model Training

The model uses deep learning to identify patterns. There are two major methods:

Supervised learning: Using labeled datasets (e.g., image + category)

Self-supervised learning: Discovering structure without labels (e.g., predicting the next word)
Models are trained using thousands of GPUs over weeks or months. This is the most compute-intensive phase.

3. Transformer Architecture

Modern generative AI models rely on the Transformer architecture, introduced by Google’s 2017 research. It uses self-attention mechanisms to weigh relationships between every word (or pixel), even across long sequences.

This is what powers large language models like GPT and text-to-image generators like DALL·E or Stable Diffusion.

4. Core Generative Model Types

These models can also be hybridized into multimodal systems, capable of understanding and generating across text, images, and video.

5. Fine-Tuning and Alignment

After base training, models are fine-tuned for specific use cases:

Glance AI, for example, fine-tunes generative models to recommend fashion looks based on body type, hair texture, and color preferences from a selfie.
Alignment techniques like RLHF (Reinforcement Learning from Human Feedback) help guide AI toward safer, more useful responses.

Want a visual explainer? This interactive blog breaks down the training workflow for non-technical readers.

In Summary:

Generative AI models work by learning from huge datasets, processing that knowledge with advanced neural architectures (especially Transformers), and using it to create intelligent, context-aware content—from fashion looks to customer emails.

Real-World Applications of Generative AI

Generative AI is now part of the mainstream tech stack across industries. From powering personalized shopping experiences to supporting drug discovery, the technology is redefining digital experiences for millions. Below are the sectors where its impact is most visible.

1. Fashion & E-Commerce – Glance AI's Hyper-Personalized Engine

At the forefront of AI-powered shopping, Glance AI enables users to upload a selfie, choose preferences (body type, tone, hair type), and receive AI-generated fashion looks in a dynamic, magazine-style feed.

Key features include:

• Style-based recommendations using generative avatars
• AI-styled clothes and matching accessories
• “Shop the look” with product tile links to PDPs
• AI Look feature powered by digital twins

This shifts the user journey from search-driven to discovery-led—perfect for India’s mobile-first shoppers.

2. Marketing and Content Automation

Marketers use tools like Jasper, Copy.ai, and Notion AI to:

• Auto-generate ad copy, blog posts, and CTAs
• Produce multiple versions of content for A/B testing
• Maintain brand tone while increasing content velocity

This supports scalable personalization, especially in email marketing, product descriptions, and landing pages.

3. Healthcare and Pharmaceutical R&D

Generative AI is being used to:

• Simulate drug molecule interactions (e.g., Insilico Medicine, Google DeepMind AlphaFold)
• Generate synthetic health records for model training
• Summarize diagnostic reports using fine-tuned LLMs

This reduces time to market for new therapies and streamlines medical communication.

4. Media, Film, and Gaming

Creative studios and platforms like Netflix and Runway AI use generative tools for:

• AI-generated scenes, voices, and visual effects
• Dynamic scriptwriting or voiceovers
• Localization of media assets for global distribution

For creators, it means quicker pre-visualization and richer storytelling without heavy manual work.

5. Education and Upskilling

AI tutors like Khanmigo by Khan Academy and Duolingo Max use LLMs to:

• Simulate real conversations in language learning
• Explain concepts using custom analogies
• Create student-specific quizzes and revision content

This allows adaptive learning at scale while keeping the human teacher in the loop.

6. Financial Services and Legal Tech

Firms use generative AI to:

• Draft contracts and agreements
• Summarize earnings calls and investor reports
• Automate compliance reports and documentation

This saves hundreds of hours and enhances operational precision in industries governed by regulation.

For more examples of how AI is used in shopping, visit our AI Shopping Guide or explore how AI personalizes fashion commerce.

Benefits of Generative AI

As AI moves from back-end automation to front-line experiences, generative models are driving major shifts in how businesses create, scale, and connect with users. Here’s why this technology is becoming foundational in 2025.

1. Hyper-Personalization at Scale

Generative AI enables brands to create tailored experiences for every user. Platforms like Glance AI do this through daily-updated AI-generated looks based on individual personas, body types, and style preferences.

This dramatically improves:

• Product relevance
• Time spent on site/app
• Session-to-purchase conversion rates

2. Faster Content Creation

From marketing copy to UI design elements, generative AI reduces turnaround time by helping teams ideate, write, and iterate at scale.

Example tools:

• Jasper for content
• Canva AI for visual assets
• GitHub Copilot for coding

Marketers can auto-generate AI-styled product descriptions or tailor banners to specific demographics without starting from scratch.

3. Cost-Efficiency

Startups and lean teams can now compete with enterprise-level output. Instead of hiring multiple teams for design, copy, or development, a few skilled operators + AI tools can launch campaigns, apps, and even MVPs faster and cheaper.

AI isn't just a productivity boost—it's an economic enabler.

4. Always-On Creative Testing

A/B testing used to be limited by creative resources. Now, AI can generate:

• Multiple ad variations
• Dynamic product headlines
• Alternate CTAs per audience cohort

This allows real-time optimization based on engagement metrics.

5. Democratizing Access to Tools

Users without technical or creative backgrounds can now:

• Create logos
• Draft resumes
• Simulate fashion avatars
• Compose music or edit video

Platforms like Glance are embedding these tools into mobile-first interfaces, making AI accessible and visual for all.

Want to explore a broader list of AI use cases in fashion and commerce? This is just the start.

Challenges and Risks of Generative AI

While generative AI opens new doors for creativity and automation, it also brings risks that require proactive governance. From bias in training data to misuse for misinformation, here are the biggest challenges to address in 2025.

1. Algorithmic Bias and Representation Gaps

AI models inherit biases from the data they’re trained on. If datasets underrepresent certain groups, the outputs may reinforce stereotypes—especially in visual content like AI-generated fashion looks.

Example: A model trained mostly on Western fashion may ignore regional styles or darker skin tones unless specifically corrected.

Mitigation:

• Diversified training data
• Inclusive persona modeling (as done by Glance AI)
• Continuous testing for demographic parity

2. Misinformation and Deepfakes

Generative AI can produce plausible but fake content—from fake news headlines to synthetic voices. As AI-generated media becomes harder to distinguish, users may fall prey to deception.

Example: Deepfake influencers or AI-written product reviews skewing consumer trust in e-commerce.

External Read: MIT's coverage of AI-generated misinformation

3. Copyright Conflicts and IP Ambiguity

Much of today’s AI is trained on publicly available internet content. This raises unresolved questions about:

• Ownership of AI-generated content
• Use of copyrighted data for training
• Attribution obligations

Governments and platforms are actively working to define the legal frameworks, but for now, tread cautiously with commercial use.

4. Privacy Leaks

Some LLMs may accidentally reproduce sensitive information if training data was scraped without adequate filtration. This becomes especially risky in sectors like finance, health, or enterprise communication.

5. High Energy Consumption

Training models like GPT-4 or Stable Diffusion require vast computing power, often powered by non-renewable energy. According to a  Google AI paper, training a single large model can emit as much CO₂ as several flights.

Solution: Optimization techniques, smaller model architectures, and green data centers.

Brands like Glance AI are already addressing some of these risks through fine-tuned models, persona inclusivity, and ethical UX design. See how it aligns with AI shopping personalization principles.

The Future of Generative AI

Generative AI is evolving from content creation to contextual co-creation—where models understand who you are, what you need, and how you want it delivered. Here’s what the next wave looks like in 2025 and beyond.

1. Multimodal Models Become the Norm

We're now entering the era of multimodal AI—models that can process and generate across multiple formats (text, images, audio, video). Tools like GPT-4o and Google Gemini already support seamless transitions between writing, visuals, and voice.

Use Case: You describe a brunch outfit verbally and show a reference image—your Glance AI avatar generates matching looks instantly with shop-similar filters.

2. Rise of AI Twins & Digital Personas

Glance AI is leading the charge with AI avatars or “digital twins”—custom visual representations of users based on selfies, hair type, and body shape. These twins will evolve into smart style assistants that understand your seasonal preferences, cultural leanings, and wardrobe gaps.

Experience this in action: AI avatars in ecommerce

3. Embedded, On-Device AI

The next leap in user privacy and speed is happening on-device. Instead of pinging the cloud, models will run locally on phones and wearables—powering offline fashion suggestions, private journaling apps, or localized language models.

For mobile-first markets like India, this means low-data AI without sacrificing personalization.

4. AI-Native Interfaces Will Dominate

Interfaces built for AI—not just with AI—will define category leaders. That means:

• Visual feeds generated in real time (like Glance AI’s magazine layout)
• Personalized ambient experiences on Connected TVs
• Real-time interaction across widgets, avatars, and predictive prompts

AI-native = predictive, conversational, visual, ambient.

5. Optimized for AIO and Voice Discovery

Google’s AI Overviews (AIO) are pushing content to be skimmable, structured, and voice-friendly. This includes:

• Schema markup (FAQ, How-To)
• Summary blocks per H2
• Answer-first formats

Tip: Blogs like 5 ways you use AI without knowing it are ideal for AIO if structured correctly.

Key Insight:

The future of generative AI is not just smarter—it’s more situated. Context-aware, real-time, ethically grounded, and built for everyday interaction. Brands that design for AI-native environments—like Glance—will shape user behavior for the next decade.

Conclusion: The Age of Generative Intelligence

Generative AI is no longer optional—it’s embedded in how we live, shop, work, and create. From AI avatars that dress you in seconds to voice-driven interactions on ambient screens, it’s clear: the brands shaping the future will be those that are AI-native at their core.Platforms like Glance AI are leading by building experiences that feel personal, predictive, and delightfully interactive.

If you're a business, creator, or consumer, the call is clear:

• Explore generative tools
• Experiment with content formats optimized for AI Overviews (AIO)
• Embed AI into your workflows, not just your tech stack

Generative AI isn't about replacing people—it's about empowering them.

FAQs 

1. What is the difference between AI and Generative AI?

AI is the broad category of machines simulating human intelligence for tasks like prediction, recognition, or automation. Generative AI specifically refers to models that create new content—text, visuals, audio, or video—from learned patterns.

Explore more in our introductory blog on Generative AI.

2. How does Generative AI generate content?

Generative models—like GPT-4 or DALL·E—use neural networks trained on massive datasets. These models predict and generate content by learning patterns in text, visuals, or audio. For visuals, they often rely on GANs or VAEs; for text, Transformers are key.

See our step-by-step breakdown.

3. Is Generative AI safe to use?

Yes—if deployed ethically. Platforms like Glance AI use safeguards like on-device processing, ethical data usage, and real-time moderation. Risks like deepfakes or misinformation exist, but can be mitigated through watermarking and transparency.

Read: MIT’s overview of safety concerns

4. What industries use Generative AI?

Key adopters include:

• Fashion & Retail: Glance AI avatars & digital styling
• Healthcare: AI-generated health records & diagnosis notes
• Marketing: Content automation and A/B testing
• Entertainment: Scripts, dubbing, media localization
• Education: Smart tutoring & curriculum generation

5. Can I use Generative AI for free?

Yes. Many platforms offer free tiers:

• ChatGPT by OpenAI
• DALL·E, Bard, Canva AI
• Glance AI offers free fashion look generation, sharing, and wishlist building.

Advanced features like commercial licensing or try-on features may require upgrades.

6. How is Glance using Generative AI?

Glance AI combines AI avatars, real-time styling suggestions, and curated product feeds into a discovery-first shopping experience. Users can:

• Upload a selfie and select style preferences
• View refreshed AI-generated looks daily
• Shop similar items based on each curated look
• Soon, try clothes on virtually using their AI twin

Explore how this works: AI-powered shopping personalization

7. What is an AI Twin or Digital Twin in Fashion?

It’s a virtual version of you—generated via selfie and style inputs. Glance AI uses this to:

• Create daily looks tailored to your avatar
• Recommend clothes for your body type and tone
• Enable try-ons before purchase (coming soon)

Learn more: Complete guide to AI stylists

8. How can I learn more about Generative AI?

Explore expert sources like:

• OpenAI Research
• Google AI Blog
• NVIDIA’s Gen AI Glossary
• Glance.com Gen AI blogs