Generative AI vs LLMs: definitions, differences and everything else businesses should know

Generative AI news and topics are much abuzz today, and for good reason. With generative AI having taken the world by storm with tools such as ChatGPT, businesses are keen to know how they can also adopt AI-powered technologies to automate common tasks, and reduce bottlenecks.

Large Language Models (LLMs) are also another key component of generative AI, and both technologies are often confused by many. Here, we break down the definitions of generative AI and LLMs, while outlining what sets each of them apart.

Additionally, we’ve also included some evergreen tips on how to always choose the right set of technologies (AI or not) for any business use case, so organisations and their software development teams always know what to seek.

Looking for generative AI or LLM solutions for your software project? Look no further! At EFutures, our team of AI experts can assess, architect, develop and implement AI-powered applications at scale, based on your business’s unique objectives. Talk to us today, to learn how.

What is generative AI?

Generative AI is a derivative from AI technologies such as machine learning, deep learning and Natural Language Processing (NLP) that learns from existing datasets and generates text, images, videos, and audio, based on customised prompts. OpenAI’s ChatGPT and Anthropic’s Claude are popular examples of generative AI applications, as they offer a prompt-based approach to generating content.

Generative AI applications have exploded in popularity, especially with ChatGPT’s public release where anyone is able to access and experiment with it, based on unique and niche prompts. Henceforth, numerous other competitors have mushroomed in the AI marketplace, providing professionals and organisations alike the ability to implement AI for any use case pertaining to content generation, through SaaS tools or APIs that are easy to integrate with existing applications.

Software outsourcing companies have also embraced AI-based software development, including building custom AI-powered applications for their clientele. Whether it’s through resources delivered via partnerships with a cloud service provider, or through the integration of a SaaS-based AI tool, many top offshore software development teams have scaled to offer their clients the leverage they’ve been looking for, through AI and machine learning.

What are Large Language Models (LLMs)?

Large Language Models or LLMs are also derivatives of machine learning, which can comprehend language and deliver outcomes that are highly contextualised. LLMs, as a result, form the foundation for generative AI, as they involve understanding and being trained on vast amounts of data (also known as big data), in order to be able to generate relevant outcomes based on prompts entered by users.

Likewise, LLMs are also essential components for Conversation Intelligence (CI), sentiment analysis and NLP, as all these AI subsets focus on understanding and delivering responses that are highly contextualised, and therefore, as close as possible to how a human would respond.

What are the differences between generative AI and LLMs?

Generative AI and LLMs have much overlap, as the field of AI typically involves the use of multiple AI technologies and models for any given use case. In spite of this, the LLM vs generative AI debate is still valid, as numerous factors distinguish one from the other. These include:

LLMs focus on understanding language patterns, while generative AI focuses on creating new content: However, it is important to keep in mind that generative AI models are significantly based on LLMs, as generative AI tools need to be able to discern context that is conducive to human interactions, in order to generate accurate outcomes.
Generative AI tools, as their name suggests, focus on content creation use cases, while LLMs focus on understanding language for a wider variety of use cases: Text, image, video and audio generation is one use case for LLMs, however it can also be applied for conversational AI needs, such as intelligent chatbots and sentiment analysis.

With so much overlap between generative AI and LLM tools, a reliable AI-based software outsourcing company, including an offshore AI and ML company shall be able to advise on which combinations are best for your business’s unique needs. Additionally, other AI subset technologies such as Robotic Process Automation (RPA) may also be useful, especially if intelligent workflow automation and routing needs to be incorporated into content generation and language understanding use cases.

Generative AI or LLMs: which AI technologies are right for your business needs?

If your business is completely new towards building AI-powered applications, trying to understand which combination of AI technologies are most suitable may be the first thing on your mind. This is also applicable to companies that already have AI-powered tools implemented, as technologies are constantly evolving and it’s imperative to stay aware of what’s new, in order to stay in lockstep with a highly competitive business landscape.

While it is natural to focus on which AI technologies are best for your business’s specific needs, owners and leaders can benefit from tweaking the way they think about and approach this topic. Instead, focusing on what your company’s unique objectives and problems are, and how they can be solved, is bound to be more fitting. This is because the AI tools that are eventually selected are based on addressing the issues and needs your company has, instead of forcefully implementing a technology into existing business systems only because it is new and cutting-edge.

Here are some evergreen tips to always choose the right tools for your business and its software projects (AI or otherwise):

Discuss existing challenges, as well as objectives that your business is looking to achieve, with relevant members of your team,
Start small, preferably with an MVP (Minimum Viable Product). This way, your team can test how suitable new deployments are before expanding them, while also being a great way of testing new offshore AI developers your organisation may have just onboarded,
Measure the effectiveness of your AI tools and any other AI-powered technologies with the right analytics tools and KPIs, so that data-driven improvements can be made.

To wrap up…

AI-based technologies such as generative AI and LLMs have skyrocketed in popularity, owing to how advanced they’ve become, with only a prompt or two required to generate high-quality results that emulate human context. Although generative AI and LLMs feature many overlaps, the latter forms the basis for the former, thereby giving generative AI tools such as ChatGPT and Claude the power that they currently have to generate highly contextualised responses to any prompt.

Generative AI, LLMs and other AI subset technologies such RPA, NLP and neural networks are all suitable for practically any use case, thereby giving businesses the leverage they need to intelligently automate almost any task in the workplace.

Determining which combination of tools are ideal is best done through a careful business assessment with relevant team members, so that AI strategies are built based on what will truly help a business achieve its objectives. An assessment shall also help determine any specific criteria to hire an AI developer, should a business/project ever require one.

FAQs

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Q1: Do AI engineers specialise in developing solutions with all types of AI?

A: AI engineers can specialise in any type of AI; the focus isn’t generally on the ‘type’ or ‘subset’ of AI technology that will be used for an AI-powered effort, but what the objectives are, and how these can be attained. Therefore, comprehensively assessing your business’s existing needs and challenges is highly essential, as this shall help your team of AI engineers determine which AI tools and technologies are most suitable to meet organisation objectives.

Q2: Generative AI vs predictive AI: what is the difference?

A: Generative AI uses AI technologies (as its name suggests) to ‘generate’ new content - while predictive AI uses any AI-based technology to identify and forecast trends based on historical data. In other words, predictive AI will not generate any brand new content, except for the predictions it makes based on all the data it currently has.

Predictive AI is a big use case for discriminative AI, as a result; discriminative AI, unlike its generative AI counterpart, understands boundaries and only classifies data based on what it knows - as opposed to making assumptions from data and creating something brand new.

Q3: What are the challenges of generative AI?

A: Albeit being a breakthrough in the history of AI technologies, generative AI also has its share of challenges, which mainly include:

● Hallucinations: this involves generative AI tools fabricating patterns, and subsequently generating information that’s false, or completely irrelevant,

● Risk of data leaks: Confidential data can be inadvertently leaked as generative AI models ingest data and train from prompts, to subsequently output it during a prompt made by someone else,

● Model manipulation: Generative AI models can be manipulated to deliver false or biased information, with repeated prompting.

However, partnering with generative AI companies can enable organisations to deliver custom models that can help mitigate these limitations, as models can be built to circumvent said limitations, as well as deploy protections which prevent data leaks from occurring.

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