What is Generative AI? 5 Best Generative AI Tools in 2024

Relia Software

Relia Software

Cong Dinh

Relia Software

Software Development

Generative AI is a branch of AI that aims to train computers to create content that is uniquely their own. Top generative AI tools: GPT-4, Bard, Claude, etc.

What is Generative AI

Table of Contents

Artificial Intelligence (AI) has been a buzzword across sectors over the previous decade, resulting in tremendous breakthroughs in technology and operational efficiencies. But as we explore the world of AI more deeply, we need to recognize and comprehend its many varieties. 

Generative AI is a type of AI that has shown great promise for disrupting established markets. However, how does it diverge from more conventional AI? Let's explore further details about this type of AI and the best generative AI tools for businesses. 

>> Read more:

What is Generative AI?

Generative AI is a branch of artificial intelligence that aims to train computers to create content that is uniquely their own. Generative AI, in contrast to classical AI, can learn from data and develop content independently, without the need for predetermined rules. This innovation uses neural networks and complicated algorithms to recognize patterns and generate results that are eerily similar to those generated by human brains.

Generative AI is significant because of the radical changes it could bring to all types of businesses. From content production to software development, generative AI tools are paving the way for improved efficiency, creativity, and innovation. Companies are increasingly using these solutions to streamline their processes, minimize manual tasks, and open new possibilities that were formerly impossible.

Generative AI is a branch of AI that aims to train computers to create content that is uniquely their own.
Generative AI is a branch of AI that aims to train computers to create content that is uniquely their own.

How is Generative AI Used?

Foundation models, upon which many generative AI tools are built, are versatile and can handle a wide variety of tasks. Generative AI has many potential uses, many of which have probably not even been discovered, much less put into practice.

There are now numerous creative and lucrative opportunities made possible by generative AI's capacity to translate across different media formats, such as:

  • Text to text (TTT);

  • Text to speech (TTS);

  • Text to image (TTI);

  • Text to video (TTV);

  • Speech to text (STT);

  • Text to audio (TTA);

  • Image to text (ITT);

  • Image to image (ITI);

  • Image to video (ITV);

  • Video to video (VTV).

Certainly, many more use cases will continue to develop as firms and sectors continue to incorporate this technology into their research and workflows.

Generative AI VS. Traditional AI: What is The Difference?

Generative AI is currently a small part of artificial intelligence, a broad field of computer science. Naturally, generative AI has many similarities to traditional AI. There are also clear differences.

Shared Characteristics

  • Both require large amounts of data to train on and make decisions (though generative AI's training data might be several times bigger). 

  • Both gather "knowledge" from collected data and utilize it to forecast the future and modify their actions accordingly. 

  • Both can be improved over time by modifying their parameters based on feedback or new information.


Traditional AI

Generative AI

Built to outperform humans at a specific task, such as spotting credit card fraud, calculating driving routes, or steering the wheel.

Generates fresh and unique content that resembles but is not present in its training data.

Traditional AI systems, such as machine learning systems, are often trained with data specific to their intended function.

Require massive amounts of data to learn from (and then, sometimes, being fine-tuned with much smaller volumes of data associated with a given function).

Often employs supervised learning algorithms to train on labeled or categorized data.

Always needs to be trained, at least initially, with unsupervised learning (where data is not categorized and the AI program is not given any explicit instruction).

The capabilities and application in which they are used distinguish traditional AI from generative AI. Generative AI goes beyond traditional AI systems by actually creating new data that is similar to its training data, whereas traditional AI systems are just used for analysis and prediction.

To put it another way, classical AI excels at pattern recognition, but generative AI shines when it comes to pattern generation. While traditional AI may examine data and report its findings, generative AI can use the same data to generate completely novel results.

What are Generative AI Tools?

Generative AI tools are software or systems that use neural network techniques like generative adversarial networks (GANs) or variational autoencoders (VAEs) to analyze data and generate meaningful outputs based on user input.

Creative output such as videos, music, text, images and others can be generated by generative AI tools.

Types of Generative AI Tools

>> Read more: Top Mobile App Development Trends for 2023

Many options exist for specific uses among the many generative AI tools available.


  • Translation;

  • Creative and academic writing;

  • Code writing;

  • Genetic sequencing;

  • Grammatical correction or analysis.

Audio and Speech:

  • Composing and songwriting;

  • Dubbing;

  • Dictation and transcription;

  • Speech and voice recognition;

  • Sound editing.

Visual and Imagery:

  • Illustration;

  • Infographics;

  • 3D modeling;

  • Creative design;

  • Image editing;

  • Architectural rendering.

Data Generating: Producing synthetic data for use in training AI systems.

Industries Which Are Currently Applying Generative AI Tools

Industries are employing generative AI tools in many ways that will grow as the technology and our understanding of it advance. Examples of current generative AI applications in many fields:

Entertainment and Media: Generative AI tools automate content generation for writers, marketers, and content creators, optimizing processes and saving 40% of content creation time. They may concentrate on strategic tasks and innovation with this efficiency boost.

Education: AI can develop course materials, lesson plans, online learning systems, and chatbots for one-on-one teaching.

Meteorology and Climate: AI can predict weather, simulate natural disasters, and model climate scenarios.

Art and Design: Artists and designers are pushing creativity by using generative AI tools to create new visual art, illustrations, and designs. A study indicated that using generative AI tools in design increased the quantity of creative and eye-catching design concepts by 75%.

Software Development: Generative AI is changing code writing. It helps developers generate code snippets, improve software testing by finding 30% more flaws, and find optimal coding solutions. Higher code quality and faster development cycles arise from these characteristics.

Translation: Real-time generative AI tools translate text and speech, enabling multilingual communication. Global communication and comprehension are enhanced by these products' 95% translation accuracy.

Healthcare: Generative AI analyzes medical imagery, diagnoses diseases, and predicts patient outcomes. Radiologists employing generative AI tools for image processing detected tiny anomalies 20% more accurately, leading to faster and more accurate diagnoses.

Gaming: Game developers use generative AI tools to create immersive virtual worlds, produce in-game content, and adapt gameplay to player activity. Dynamic adaptation boosts player involvement and pleasure by 50%, improving the gaming experience.

Finance: Financial organizations use generative AI tools to study market trends, anticipate stock movements with 85% accuracy, and optimize trading methods. This technology-driven approach has increased trading profitability by 25% and improved investment selections.

Benefits and Drawbacks of Using Generative AI Tools


Generative AI tools serve businesses mostly through knowledge synthesis, human-AI collaboration, and speed. Many of the benefits listed below are comparable to those promised by earlier AI models and automation tools. However, one or more of these three features can help businesses achieve them sooner, simpler, and more successfully.

Increased Productivity

Generative AI tools can help knowledge workers save time on mundane tasks like learning a new skill for a project, organizing data, etc. Using generative AI, fewer workers may complete jobs that formerly needed big teams or hours in a fraction of the time. A generative AI tool may detect faults in minutes and offer changes, while a team of programmers may spend hours debugging incorrect code. 

Cost Savings

Generative AI tools are fast, so if a task takes half the time, it costs half as much. Generative AI also reduces errors, downtime, and redundancies and other inefficiencies.

Improved Customer Satisfaction 

Generative AI–based self-service and generative AI tools infuse real-time knowledge into customers for a better, more personalized experience. 

Better-Informed Decision-Making

Well-trained, enterprise-specific generative AI models can provide extensive insights through scenario modeling, risk assessment, and other predictive analytics methods. 

These tools can help decision-makers understand their industry and the business's position in it by providing personalized recommendations and actionable strategies based on more data and faster analysis than human analysts or older technology.

Faster Product Launches

Generative AI tools can swiftly create product prototypes and early drafts, fine-tune works in progress, and test/troubleshoot current products to uncover improvements.

Quality Control 

An enterprise-specific, specialized generative AI model may reveal defects in a company's user manuals, videos, and other public content.



Even professionals can be misled by generative AI models' detailed and authoritative information. The model just predicts the next word, thus it can extrapolate from its training data to assert falsehoods as authoritatively as truths. 

Computational Power and Initial Investment

Generative AI models demand tremendous computing power for training and operation. Companies often lack the resources and knowledge to design and manage these systems. Generative AI development often uses cloud infrastructure for this reason.

Reduced Output Quality and Originality 

Organizations that use public generative AI tools instead of building their own models may be doomed to mediocrity. They often get the same findings as others since they use the same training data. Unless these organizations incorporate human creativity, they may conform to best practices but struggle to develop a competitive edge.

Generative AI makes producing products and content faster and easier, but it doesn't ensure quality. AI algorithms without human collaboration may lead to uniform, uncreative goods.

Resistance from Workers and Customers

Long-time employees with engrained protocols and processes may struggle to adapt to generative AI tools, reducing productivity. Staff may also fight technology to avoid losing their jobs. Managers and executives must calm these worries and be upfront about how technology will change – or not change – the business structure.

Privacy/Intellectual Property

Generative AI models commonly learn from prompts. Businesses, especially medical clinics that acquire sensitive consumer data, must secure IP and confidential data. This information may raise exposure if the model accesses it.

>> Read more:

Top 5+ Best Generative AI Tools in 2023

1. GPT-4

After the success and broad acceptance of GPT-3 and GPT-3.5, OpenAI created GPT-4, its latest Large Language Model (LLM). GPT-4 is said to be more innovative, accurate, safe, and stable than prior versions.

GPT-4 improves AI language models' text production across domains. Writing, marketing, and education benefit from its content generating skills. Data analysis is improved by its versatility in natural language processing.

Key Features:

  • Has 100 trillion parameters.

  • Large multimodal model.

  • Accepts both image and text inputs.

  • Emits text-only outputs.

  • Engine behind ChatGPT Plus.

  • Available via ChatGPT and API waitlist.

  • Multilingual capability.

  • Produce more coherent and nuanced responses.


  • This latest GPT release is more stable than prior ones.

  • GPT-4 did well on standardized exams, such as the bar exam and numerous AP tests.

  • GPT-4 features more safety checks and training than earlier versions, including pre-training data filtering, expert reviews, model safety improvements, and frequent monitoring.


  • Public image inputs are unavailable.

  • Questions remain concerning the technology's stability and safety.

  • Still sensitive to training data biases.

  • GPT-4 has improved but still has bias in several outputs.


  • Free Version.

  • Prompt: $0.03/1,000 tokens.

  • Completion: $0.06/1,000 tokens.

  • Paid membership: $20/month.

GPT-4 is said to be more innovative, accurate, safe, and stable than prior versions.
GPT-4 is said to be more innovative, accurate, safe, and stable than prior versions. (Source: Internet)

2. Bard

Bard, Google's reaction to ChatGPT, is a transformer-based chatbot and content generator based on LaMDA. Since the tool is still technically an experiment, Google is only making it available to a small subset of people in the US and UK for now.

The tool has fewer functionality than ChatGPT, but it's expanding. Coding and development necessities such as debugging, generating, and explaining code are all things that Bard was enhanced to handle in late April 2023.

The AI Bard chatbot from Google combines the world's information with massive language models' power, intellect, and creativity. It uses web information to provide fresh, high-quality responses, is capable of following instructions and completing requests thoughtfully, answering questions, and generating poems, code, scripts, musical pieces, email, and letters.

Key Features:

  • Based on transformer-based Language Model for Dialogue Applications (LaMDA).

  • Response rating by users.

  • Accessed via personal Google accounts.

  • Waitlisted for limited US and UK users.

  • Supports programming and software development.


  • A result of Google's ethical and transparent AI development.

  • Thousands of testers trained and gave Bard comments before its launch.

  • Bard uses the internet, therefore its responses are less likely to be obsolete than ChatGPT's.


  • ChatGPT has conversational history, while Bard does not.

  • Users with Google Workspace admin accounts cannot use this feature.

  • Bard is solely in English, while ChatGPT is multilingual.

Pricing: Free (limited users).

>> Read more: ChatGPT, GitHub Copilot, and Tabnine: Top AI-Driven Tools

3. Claude

The $5 billion AI business Anthropic offers Claude, one of its latest chatbot aides and content creators. The program is similar to ChatGPT but focuses on safety and a customizable, conversational tone. Early users lauded Claude's comedy, content creation, and communication style analysis.

Quora, DuckDuckGo, Robin AI, and Juni Learning were early Claude testers. Claude helped build Notion AI, the generative AI assistant integrated to Notion project management.

Key Features:

  • Based on Constitutional AI.

  • Available in Slack.

  • The product has two versions: Claude Instant and Claude Classic.

  • Developers can use backend API.

  • Support for many popular spoken languages and programming languages.

  • Processing large amounts of text data well.

  • Natural, fluid discourse.

  • Automates difficult workflows.

  • User feedback is seamlessly adapted for continual improvement.


  • Possibly bigger context window than OpenAI models.

  • Claude has red-team prompts to prevent hazardous content.

  • Sometimes Claude seems self-aware and gives users detailed explanations when it cannot answer a question.


  • If the user contextualizes their request as fiction, harmful requests can pass.

  • No free version of Claude exists.

  • Internet access is unavailable to Claude.

  • Its complexity makes it difficult.


Claude Instant:

  • Prompt: $1.63/ million tokens.

  • Completion: $5.51/ million tokens.


  • Prompt: $11.02/ million tokens.

  • Completion: $32.68/ million tokens.

Claude helped build Notion AI.
Claude helped build Notion AI. (Source: Internet)

4. Synthesia

The AI video creation platform Synthesia lets users make videos using programmed prompts. The application then uses its AI avatars, voices, and video templates to generate a lifelike video. This tool requires no video editing expertise or equipment.

Product marketing, training, and how-to videos for internal and external users are most often created with Synthesia. For beginners, Synthesia offers example videos, a support center, and Synthesia Academy tutorials.

Key Features:

  • The AI-powered platform rapidly translates text to videos.

  • There are over 125 AI avatars.

  • There are 120+ languages and accents.

  • Users can construct AI dynamic avatars and scenes.

  • AI-powered voice synthesis for smooth audio.

  • Visuals, text, and voice can be customized.

  • Users can choose pre-made video templates.

  • Quickly create several videos simultaneously.

  • Intercom, Mindstamp, PowerPoint, WordPress, Vimeo, and HubSpot Knowledge Base integrate.


  • Customizes videos to individual preferences by extensive options offered.

  • Offers one free AI video to potential buyers.

  • Videos can be made in 120+ languages.

  • Users find the tool simple and accessible.


  • Authoring environment bugs can occur.

  • Without the premium plan, audio is a little off.

  • Some users have reported support issues.

  • Videos can lack art without human contribution.



  • ₹1499.92/month, ₹17,999.00 billed annually, or pay monthly.

  • $30 per month, billed monthly.

Enterprise: price based on the number of seats.

5. DALL-E 2

As a research project, OpenAI developed DALL-E in January 2021, which can create graphics using natural language. It introduced DALL-E 2, which combined concepts, attributes, and styles, a year later. 

DALL-E 2 is OpenAI's latest picture and art-generating AI tool. Users can prompt DALL-E 2 to make a new image or edit an existing image.

DALL-E 2 generates more lifelike graphics that match user requests than DALL-E. Additionally, DALL-E 2 appears to have gotten greater training on how to reject unsuitable inputs and prevent problematic outputs than its predecessor.

Image synthesis is Dall-E2's forte. Translating words into appealing graphics allows artists and designers to experiment. Unique artwork and personalized images are notable triumphs. It bridges the text-image gap and spurs innovation despite occasional mistakes and limits in its learned notions.

Key Features:

  • Phased learning deployment.

  • Natural language inputs for producing visuals and art outputs.

  • Multiple variations on the same image are possible.

  • With the inpainting function, users can fix up old photos.

  • The inpainting function is used to make requested changes or tweaks to an image.

  • DALL-E was designed for programmers.


  • The resolution of DALL-E 2 is four times that of DALL-E.

  • Developers can now add DALL-E to apps and products.

  • Increases efficiency and generates varied images smoothly.

  • Connects text and images.

  • Inspires user creativity.

  • Visual concepts may be prototyped quickly.


  • Creates incorrect images.

  • Pre-trained image ideas limit.

  • Possible misinterpretation of difficult text.

  • Limited real-world accuracy, needing manual corrections.

Pricing: $15 USD/ 115 credits

Tips for Building Top-Notch Generative AI Applications

Building a cost-effective, safe, compliant, and robust Generative AI application requires certain notes.

1. Set a clear objective: You must choose a Generative AI application type and problem to address. A defined goal saves time and money.

2. Select appropriate tools: Choose the correct third-party APIs, models, integrations, MLOps, monitoring, and automation tools. Project success depends on selecting the correct tools for your application.

3. LLMOps matters: AI rules need model monitoring and maintenance in production. Focusing on LLMOps reduces operational expenses and ensures end-user stability and security.

4. Follow security rules: Checks for prompt injections, data leaks, and other malicious activities. User prompts should be validated and sanitized to meet industry standards and avoid legal issues.

5. Run offline model tests: Testing LLMs offline for accuracy and performance metrics before deployment is essential.

6. Start small with little skills: Start with a chatbot instead of an AI platform, then add features like uploading files, producing photos, etc.

7. Model infrastructure: Cost study is necessary before using cloud services for your application because LLMOps is expensive. Companies usually waste money since they don't know memory and computation requirements.

8. Track manufacturing of your model: Check for bias, malicious use, and performance degradation. Prepare a rollback plan.

>> Read more: Roadmap To Become A Machine Learning Engineer


Generative AI has opened up new possibilities, where machines are actively creating rather than just copying humans. The generative AI tools mentioned in this blog demonstrate technology's rapid advancement in code snippet generation, music composition, and visual content creation.

We must embrace generative AI's power while acknowledging its limitations and need for human monitoring and improvement. These advanced generative AI tools are helping industries achieve unparalleled efficiency, creativity, and innovation.

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