/
Generative AI
Tuesday, August 5, 2025
Generative AI: Key Tools, Popular Models, and Practical Applications
As experts in AI automation, we’ve worked closely with clients across multiple industries to integrate generative AI tools into their workflows. From marketing and sales to recruitment and HR, our use cases prove that generative AI can accelerate content creation and decision-making in many scenarios.
In 2024, over 70% of organisations integrated generative AI into at least one part of their business, more than double the rate seen just a year earlier. With such rapid growth comes a natural curiosity about the technology driving it.
In this article, I’ll break down its core components and applications, and help business leaders, product managers, and engineers understand where to implement the gen AI tools.
What Does Generative AI Do?
Generative AI produces content or even synthetic data based on user prompts and training data. What differentiates it from traditional AI models, which recognise patterns or classify data, is that it actively creates new data.
Generative AI works by training deep learning models, like neural networks, on massive datasets to generate original outputs. These models learn the structure and nuances of their training data and then mimic human-like outputs.
Understanding the Technology Behind Generative AI
Generative AI is built on a combination of foundational model architectures, training techniques, and applied language technologies.
Here’s what determines how generative models process data and produce outputs:
Transformer Architecture: The engine behind modern generative AI. Transformers process sequential data (words or code) using self-attention mechanisms, enabling models to understand context and generate coherent output.
Generative Adversarial Networks (GANs): An architecture that pairs a generator with a discriminator in a competitive loop, enabling the creation of convincing synthetic images, video, and other data.
Variational Autoencoders (VAEs): Used to learn and reconstruct latent representations of input data. Ideal for generating variations of faces, digits, and other patterns.
Recurrent Neural Networks (RNNs): Early sequence models for language, speech, and music generation. Now largely replaced by more efficient transformer models.
Diffusion Models: Used primarily in image generation, these models create visuals by gradually refining random noise into meaningful outputs. AI generative tools like DALL·E 2, Midjourney, and Stable Diffusion rely on this technique.
Generative models are trained on massive datasets, often using unsupervised or self-supervised learning techniques. These models learn to recognise patterns and structures in the data, enabling them to generate new outputs that closely resemble the original content.
Reinforcement Learning with Human Feedback (RLHF): A fine-tuning process that incorporates human judgments to improve how the model behaves. It's commonly used to align chatbot responses with user expectations.
These core architectures and training techniques form the technical foundation of generative AI.
Higher-level technologies, such as NLP and LLMs, are built on top of these systems and enable real-world capabilities like chatbots, translation, and document summarisation:
Large Language Models (LLMs): Applications of transformer architecture trained on vast text datasets to simulate human-seeming interactions.
Natural Language Processing (NLP): A fundamental branch of AI that allows machines to interpret and produce human language. NLP techniques underpin LLMs and other language-based generative tools.
Top Generative AI Models in Use Today
Built on these foundational architectures, modern generative AI models are implemented across different modalities, such as text, image, code, audio, and more.
Text-Based Generative Models
These models generate language that’s both logical and context-aware. They power chatbots, write marketing copy, draft reports, and support multilingual communication at scale.
GPT-4 (OpenAI): Capable of advanced reasoning, text completion, summarisation, and conversation.
Claude (Anthropic): Focused on safe and aligned responses that are useful for enterprise use cases.
Llama 4 (Meta): Open-source language model used across various AI apps for content generation and reasoning.
Image Generation Models
These models create highly detailed images from text prompts. Industries like design, marketing, gaming, and e-commerce use generative AI applications to produce visual content quickly and at scale. Depending on the model, outputs can range from realistic product visuals to stylised concept art.
DALL·E 3 (OpenAI): Known for producing artistic and high-resolution visuals from complex prompts.
Midjourney: Focuses on creative styles and is widely used in design and concept art.
Stable Diffusion: Open-source image generator valued for its customisation and community contributions.
Code Generation Models
These tools are designed to assist in software development by generating or completing code. They’re commonly used in IDEs, CI/CD pipelines, and educational tools for developers.
GitHub Copilot (OpenAI + Microsoft): Autocompletes code, writes functions, and suggests solutions inside code editors.
CodeWhisperer (Amazon): Trained on large codebases to assist developers across various languages.
StarCoder (BigCode): An open model for code generation with strong multi-language support.
Audio Generation Models
These models are designed to create speech, music, or soundscapes from text or audio prompts. From generating lifelike voice clones for audiobooks to composing background music for videos, these tools bring sound to life in new ways.
VALL-E (Microsoft): A text-to-speech model that can mimic a speaker’s voice with just a few seconds of audio.
MusicLM (Google): Generates high-quality music tracks based on text descriptions.
ElevenLabs: Specialises in voice cloning and natural-sounding speech generation, often used in audiobooks and content dubbing.
Video Generation Models
Video models turn text prompts or still images into short videos or animated sequences. They help create promotional content, explainer videos, and visual prototypes.
Runway: A multimodal video generation tool that turns text, images, or clips into full-motion videos.
Pika: Known for fast and creative short video generation, widely used for content marketing.
Sora (OpenAI): Aims to create realistic videos from complex textual prompts.
Multimodal Generative Models
Multimodal models can process and generate outputs across different data types (text, images, audio). These cutting-edge tools are often used in advanced virtual assistants, research, and enterprise automation.
Gemini (Google DeepMind): Designed for cross-modal understanding and generation.
GPT-4o (OpenAI): Capable of processing and generating text, audio, image, and video.
Grok (xAI): Built with multimodal reasoning in mind, it integrates various input types.
What Are Generative AI Tools?
Generative AI tools are apps or platforms that bring the technology to life for everyday users and businesses. Let’s compare some top solutions:
Tool | Description | Use Cases |
|---|---|---|
ChatGPT (OpenAI) | Conversational AI powered by GPT-4o used for text generation and problem-solving. | Customer support, content writing, brainstorming |
Midjourney | AI art generator focused on high-quality, stylised visuals. Operates via Discord. | Marketing assets, product design, creative storytelling |
GitHub Copilot | AI-powered code assistant embedded in IDEs. Supports real-time code completion and debugging. | Auto-generating boilerplate code, debugging, code reviews |
Runway | Multimodal content creation suite known for powerful video generation and image tools. | AI video editing, ad content, media production |
Notion AI | Embedded assistant for writing, summarising, and organising content within Notion. | Task planning, note-taking, content drafting |
These AI tools and generative software enable businesses to reduce costs, improve operational efficiency, and stay ahead in digital transformation initiatives.
Want to explore more tools? Check out our AI agent market landscape for solutions across content, sales, customer support, infrastructure, and more.
Conclusion
Generative AI is already embedded in content creation, software development, testing, and visual design across industries. And while these tools are undeniably powerful, their real value comes from complementing human creativity rather than replacing it. We strongly recommend building a strong understanding of generative AI, training teams to use it wisely, and exploring solutions that align with operational goals.
At Easyflow, we build custom AI agents that fit seamlessly into the way your business already works. Whether it's generating SEO-optimised content, creating visuals, drafting documents, or handling repetitive tasks, generative AI is a key enabler of intelligent business automation. If you're ready to streamline operations, save time, and scale, let’s build the right solution for you.
Posted by
Viktoriia Pyvovar
Content writer