LLM vs Generative AI: How to Decide What Works Best for You?

Artificial Intelligence plays a vital role in streamlining workflow, building innovative products, getting customer insights, and offering enhanced user experience. Moreover, you might have often found businesses debating on LLM vs Generative AI. Both technologies are revolutionizing industries and opening doors for new possibilities, particularly for everyone involved in AI ML Development.

LLMs are specially designed to interpret and generate human-like text, which makes them suitable for NLP tasks such as conversational AI and language-based apps. On the contrary, Generative AI specializes in developing content in diverse formats. 

Even though both technologies look similar, they have many differences. Understanding the difference between LLM and generative AI is crucial for businesses, developers, and AI enthusiasts. 

In this blog, we will compare generative AI vs large language models in detail. But before that, let’s understand the basics of what is generative ai vs llm.

What is Generative AI?

Generative AI is an essential category of artificial intelligence that generates new and original output in text, images, and videos by analyzing existing data and considering its foundational patterns. Compared to traditional AI, which examines and classifies information, Gen AI can produce output that represents the real world, making it an excellent tool for any creative or technical applications.

At its core, Gen AI architecture uses several highly advanced neural networks, such as Generative Adversarial Networks (GANs) and Transformer-based models, to mimic human-like behavior. The two models work in sync, one focusing on producing content while the other focusing on optimizing it. With this architecture, Gen AI becomes progressively better at producing human-like outputs.

Applications of Generative AI

Generative AI has varied applications, each of which can improve productivity and workflow, enhance creativity, and provide a top-notch experience to end users. Hence, many businesses and new-age entrepreneurs have started opting for Gen AI services.

Let’s look at the crucial applications of Generative AI.

• Text Generation: It simplifies the process of generating articles, stories, poems, scripts, musical pieces, and emails. In short, it generates highly interactive and creative content without manual effort.

• Image Generation: This AI produces realistic or artistic images for social media, product mockups, and conceptual art from scratch or by modifying existing ones. Hence, there is a considerable demand for this in the design, advertising, and gaming industry.

• Video Generation: Generative AI leverages several best-in-class technologies to create realistic and animated video content based on the prompts entered for multiple purposes, such as marketing, adding notable effects to movies, crafting animated videos, and more. 

• Audio Generation: This AI helps you create music, voiceovers, sound effects, and more. Thus, it is ideal for multimedia projects. 

• Data Synthesis: Gen AI is great for generating synthetic datasets. These datasets are further leveraged for training AI ML models, expanding existing datasets to enhance data diversity and performance, and building anonymous datasets to maintain privacy research.

• 3D Model Generation: It is suitable for crafting next-gen 3D models for virtual reality, gaming, product designs, and more. Thus, it is responsible for simplifying the entire creative process.

Examples of Generative AI

These examples of Gen AI tools showcase how generative AI is enhancing creativity and productivity across industries, from design to corporate training. Let’s look at a few leading Generative AI tools that highlight the diverse applications of generative AI.

Midjourney: The AI-based tool is well-known for converting textual prompts into artistic, surreal, and unique styles.

OpenAI Jukebox: This AI-powered application enables content creators, musicians, and markets to generate music in various genres and artistic styles.

Synthesia: is a well-known AI-powered communication platform that allows everyone to create realistic AI-driven video avatars and professional videos without using mics, cameras, actors, or studios.

What are LLMs?

Large language models are one of the most essential types of artificial intelligence designed especially for understanding, processing, and generating human-based output. These models are trained on massive datasets, so that they can generate content with utmost accuracy and fluency.

LLMs leverage transformers for processing and generating text. These models predict the next word in a sequence or sentence, considering the previous text, and that’s why they are able to produce content that looks human.

Applications of LLMs

The deep learning model has a wide range of applications for businesses. Here are some of the trending LLM applications.

• Content Generation: Generate context-aware content according to the user query for multiple purposes, such as articles, blog posts, video scripts, social media posts, and Ad copy.
• Language Translation and Localization: The models provide real-time translation from one language to another with accuracy and contextually reliable information. Also, LLMs adapt the content according to culture and regional nuances.
• Education and Training: LLMs take the educational experience to the next level through personalized experiences and on-the-go feedback. They even produce fun quizzes, materials, and detailed notes on any complex topic as needed.
• Question Answering: These models can quickly interpret language and provide output. Hence, they are highly suitable for offering contextually precise answers to many questions. 
• Cybersecurity: Closely examine massive data to predict, detect, and respond to potential vulnerabilities and security threats.

Considering the broad applications of LLMs, various businesses and organizations have started opting for LLM development services to improve their productivity, enhance decision-making, and improve customer experience. 

Examples of LLMs

LLMs have entirely changed the way we leverage technology in our daily lives. These LLMs are responsible for highly accurate and context-aware language processing in varied industries. Here are some popular examples of LLMs that showcase the abilities of these models.

• OpenAI ChatGPT-4: The newest flagship multimodal LLM, ChatGPT-4 assists with a wide range of content creation tasks and customer support.

• PaLM 2: PaLM 2 is a top-tier LLM featuring enhanced multilingual, reasoning, and coding capabilities. It handles translation, summarization, and precise question answering.

Generative AI vs LLM: Key Differences

Let’s look at the differences between generative AI and large language models based on some of the most essential factors.

1. Scope and Focus

Generative AI

The scope of Generative AI is not limited to text; it emphasizes creating fresh content such as images, music, videos, and synthetic data. This type of AI considers learning patterns from the input data when generating content. Therefore, it is able to maintain creativity and diversity in every piece of content. In addition, generated content aligns well with real data, which is the most crucial thing across all creative industries.

Large Language Models (LLMs)

LLMs give heavy weightage to understanding, processing, and generating human-like text. While generating any content, they invest time in becoming familiar with linguistic patterns, semantic relationships, and overall context. LLMs focus on interpreting and responding to the prompts well, which makes them the ideal choice for tasks, such as translation, content creation, communication, language processing, and more.                                                                                            

2. Training Data and Learning Process

Generative AI

Generative AI is trained on a vast amount of datasets consisting of different types of media – images, audio, and text. Given diverse datasets, Generative AI understands patterns, structures, and styles. This helps Generative AI produce unique output that resembles real-world examples.

Large Language Models

Large language models are trained extensively through massive text datasets available through online and offline repositories. 

Online sources include websites, digital publications, and publications, while offline sources comprise licensed collections, print media, and proprietary data. Using these datasets, LLMs become familiar with the complexity involved with human language, such as grammar, semantics, structure, and context. Further, they leverage a transformer-based model to fine-tune data and transform how they generate coherent and relevant text. 

3. Functionality and Output

Generative AI

Generative AI’s main functionality is crafting content for multiple modalities, such as text, images, music, and videos. It leverages advanced algorithms to produce dynamic output while keeping statistics intact. This works well for applications where fresh content is needed every time, such as composing music, creating art, synthesizing speech, etc.

Large Language Model

Large language models are crucial for producing coherent, context-aware text according to user inputs or prompts. These models utilize attention mechanisms and transforms to efficiently complete tasks such as translation, text summarization, question-answering, and more. Hence, the model is great for applications involving language interpretation and quick response. 

4. Core Technologies Used

Both LLMs and Generative AI utilize some of the best technologies. Here, we will compare Generative AI vs LLMs based on the core technologies utilized.

Generative AI

Here are some of the technologies particularly leveraged for developing Generative AI applications. Let’s briefly discuss them. 

• Generative Adversarial Networks (GANs): These are made by configuring two neural networks—a generator and a discriminator. The generator’s primary purpose is to produce output based on the input and optimize it to the best of its ability. The discriminator focuses on determining whether the generated output is real or fake.

• Variational Autoencoders (VAEs): They are a class of neural networks that generate output in a compressed format before decoding it back to the original format. Due to this randomness, the model offers multiple versions of the output, which resemble real-world examples. Hence, it is excellent for tasks involving image generation and reconstruction, data compression and denoising, anomaly detection, and more. 

• Transformers: Built for natural language processing, transformers are now leveraged to generate diverse kinds of content. Transformer models understand all the words in a sentence along with their relations between them in one go, which makes them highly efficient and GPU-friendly.

Large Language Models

• Transformers: Transformers are the core of the large language models that comprise the encoder and decoder and possess self-attention mechanisms. These transformers understand the language and relationship between words and phrases by fetching meaning from a sequence of text. 

• Self-Attention Mechanism: The most essential block in the large language models enables the models to know the value of every word in a sentence. It even lets you measure the value of different parts of the input sequence and modify the output for maximum relevance and accuracy. Thus, the self-attention mechanism allows the specific LLM with language comprehension and response accuracy.

• Transfer Learning: LLMs are often pre-trained on massive datasets and later fine-tuned for particular tasks. 

• Tokenization: It is one of the most essential steps in large language models, which breaks down text into smaller units known as tokens. These tokens can be words, letters, or sequences of letters.  Further, LLMs assign a number to each token and examine how these tokens are used together. Ultimately, it produces a series of output tokens considering the input sequence.

5. Applications in Different Industries

We have already discussed the applications of generative AI and large language models. Now, compare LLMs vs Generative AI based on the practical applications of both technologies in different industries.

Generative AI

Generative AI has applications in almost every industry. Here, we will look at some of the most popular Generative AI applications in evergreen industries.

• Real-Time Fraud Detection in Finance: Gen AI is trained on many datasets. Hence, it analyzes patterns, detects anomalies or any other fraudulent activity on the go, and reports to the authorities. Thus, it helps you safeguard your financial data while remaining aware of new fraud patterns.
• AI-Driven Clinical Trial in Healthcare: AI technology makes it possible to gather massive patient data. This further speeds up drug development and the introduction of new clinical trials. It can also help you improve predictive accuracy in medical research.
• Dynamic Pricing in eCommerce: The eCommerce space is highly crowded, and customers have one or another option for buying things. Therefore, it is necessary to adjust prices as needed. Gen AI implements personalized pricing strategies for products, considering market trends, competitor behavior, and ongoing demand in real-time.
• Curriculum Design and Lesson Planning in Education: Curriculum and lesson plans need to be updated regularly to ensure that learning remains relevant to ongoing trends. Gen AI makes it easy for educators to discover the latest topics, craft the most suitable outlines, make a schedule, prepare materials, and offer students a next-gen teaching experience.

Large Language Models

• Clinical Documentation: LLMs streamline the entire process of crafting medical reports and clinic notes and ensure that the documentation is accurate and complies with the desired state guidelines. Thus, they enable healthcare professionals to focus on what matters most: patient care.

• Automated Customer Support in Finance: LLMs have the power to offer real-time, conversational support to customers in case of any doubt regarding any financial product or service. Plus, it answers any transaction queries, security issues, and more.

• Product Description Generation in eCommerce: It helps you generate engaging, accurate, and SEO-optimized product descriptions while keeping brand voice and style intact. Thus, it improves the customer experience and saves store owners time and effort.

Also Read: RAG vs Fine-Tuning

Key Ethical and Practical Challenges

Challenges in Generative AI

• Data Privacy and Copyright Concerns: Generate AI utilizes a collection of datasets from multiple online sources. This might become an issue for everyone when it starts producing output that is the same as the copyrighted material. 
• Ethical Issues: Generative AI is the reason behind deepfake technologies and tools for spreading misinformation. How?  Several people worldwide are using Gen AI to make fake content, such as deepfake (videos, images, and audio) or false information sharing online.
• Workforce Automation Concerns: As generative AI becomes smart at various creative roles, such as art, writing, and music, there is a practical concern that it might eliminate these jobs, resulting in economic problems for people involved in them.
• Quality and Bias Control: If there are any biases in training data, generative AI is likely to give biased or poor output, which can reduce its effectiveness in sensitive tasks.

Challenges in LLMs

• Bias and Ethical Concerns: LLMs have a significant dependency on the massive data sets during the training, so that they might adopt specific types of biases from the data. As a result, these models can duplicate or amplify those biases, providing outputs that might lead to societal stereotypes or discriminatory beliefs.
• Accuracy and Misinterpretation: LLMs can provide reasonably and likely true but factually false information whenever a user offers a complex input. This might become a serious concern in the case of pivotal tasks.
• Resource Intensity and Scalability: Training and launching a large language model requires sufficient computational resources. This might cause practical concerns, such as cost, energy consumption, environmental impact, accessibility, and scalability.
• Academic Concerns: Various schools, universities, and academic institutions allow students to leverage LLMs. However, students might use LLMs for generating essays, and finishing assignments. Due to LLMs, students can’t improve their critical research skills. Secondly, it raises questions about the assessment practices followed by educational institutions. 

Generative AI vs Large Language Models: A Quick Overview

When to Choose Generative AI?

Use Case

Generative AI works well in crafting unique content for multiple modalities, such as text, images, videos, and audio. Therefore, the ideal applications of generative AI comprise tasks such as marketing campaigns, video production, and art design, where producing high-quality output is non-negotiable.

Consideration

Gen AI can generate visually appealing or text-based content; however, its quality depends more or less on the datasets and how the model is configured. With high-quality datasets and fine-tuning features, you can deliver output as per the brand’s requirements.

Example 

A marketing or design agency can leverage generative AI to craft unique social media posts for multiple clients that align well with the brand’s tone and style. A great example of this would be Midjourney. The tool enables designers to generate stunning visuals with AI-based aesthetics.

Also Read: Top Job Platform Powered by Generative AI

When to Choose LLMs?

Use Case

LLMs are especially best for tasks that require natural language understanding and processing, such as the content creation, translation, summarization, and code generation. They work at the optimum level in any situation that requires in-depth comprehension of tone and context. Hence, they are suitable for building chatbots, offering writing assistance, and developing educational tools for efficient learning.

Consideration

When choosing LLMs, it’s essential to check the overall availability of superior training data and particular requirements for the application. In general, LLMs require very high computational power, detailed prompt engineering, and expertise with deployment. Also, the models offer the desired output. However, they need to be optimized regularly to achieve the same accuracy and relevance in different environments.

Example

A customer support chatbot such as OpenAI ChatGPT offers real-time answers to users’ queries, considering their earlier interactions. This improves engagement and helps achieve user satisfaction. It also resolves users’ queries with utmost accuracy without any human interaction and maintains a proper brand voice and style.

Also Read: How Intelligent Hiring is Possible with AI-based Recruitment Software

Key Takeaways from LLMs vs Generative AI

The debate about generative AI vs. LLM will not end soon. We have looked at the differences between generative AI and LLMS based on some crucial factors and challenges. Generative AI is best when you want to generate content in diverse formats, such as video, audio, text, and image. LLMs shine in natural language understanding and building interactive applications.

Apart from this, you can even leverage these technologies together for building a next-gen application.

If you have decided to leverage these technologies for your project, consider hiring AI engineers from OpenXcell. Our team has expertise in building AI-based solutions using Generative AI or LLMs. Partner with us today to develop your next-gen AI application, improve your business performance, and achieve tremendous growth.