Today’s digital landscape is evolving faster than ever, with AI technologies responsible for much of this change. Two tools have emerged as pivotal players in this new landscape: OpenAI’s ChatGPT and Google’s Search Generative Experience (SGE). While they use similar underlying technologies, SGE and ChatGPT serve fundamentally different purposes — though there has been a growing misconception that they serve identical roles. Although these tools can complement each other, it’s crucial to understand their distinct functionalities and differences in order to utilize both of them to their greatest advantage.

To emphasize the distinction between ChatGPT and search tools like SGE, OpenAI launched a search prototype called SearchGPT on July 25, 2024. Functionally similar to SGE, SearchGPT is currently in a testing phase and only available to a select group of users and publishers.

The Technology Behind SGE and ChatGPT

There are three key technologies behind these platforms: Large Language Models (LLMs), Deep/Machine Learning, and Natural Language Processing (NLP). By understanding these technologies — how they work, how they function, and how they influence SGE and ChatGPT — you can get a better understanding of the foundation.

1. Large Language Models (LLMs): Both ChatGPT and SGE rely on LLMs, which are advanced deep learning models designed to process and generate human-like text. These models predict the next word in a sequence based on the context provided by the previous words. These models are trained on extensive datasets containing a wide range of texts, which helps them produce coherent and contextually appropriate responses. For example, OpenAI’s GPT-3, which powers ChatGPT, is a prime example of an LLM. It’s equipped with 175 billion parameters (or weights and biases that the model learns from the training data), allowing it to generate highly sophisticated text.

2. Deep Learning and Machine Learning: These technologies are foundational to the development and operation of LLMs. Deep learning, which is a type of machine learning, uses neural networks with multiple layers to identify patterns and make decisions. This helps AI systems learn from data and improve their performance over time. Machine learning algorithms train these networks, improve their abilities, and continuously enhance their effectiveness with new data and feedback.

3. Natural Language Processing (NLP): These technologies enable AI systems to understand and interact with human language. NLP encompasses a range of techniques and models designed to process, analyze, and generate text. It empowers tools like ChatGPT and SGE to understand language nuances, including grammar, meaning, and context. Through NLP, these systems can perform tasks such as sentiment analysis, language translation, summarization, and more.

One of the greatest similarities between SGE and ChatGPT is their utilization of the above technologies to drive their functionalities. Past that, their benefits, limitations, and use-cases are inherently different.

What is ChatGPT?

Developed by OpenAI, ChatGPT is an advanced AI tool designed for tasks like content creation, summarization, and language translation. It leverages GPT-3, one of the most sophisticated LLMs available today. This allows ChatGPT to generate highly sophisticated and contextually relevant text based on user interactions. ChatGPT further enhances its understanding by utilizing vector embeddings (numerical representations of words or phrases that capture their meaning and relationships within context) alongside the LLM. This combination results in a powerful chatbot that can generate dialogue and answer user questions.

Strengths of ChatGPT

• Generative Content Creation: ChatGPT excels at generating contextually appropriate text based on user prompts, making it ideal for drafting emails, writing essays, and crafting stories. Marketers can use ChatGPT to create compelling ad copy, brainstorm content ideas, and refine campaign strategies. Additionally, it effectively summarizes lengthy documents, extracting key points and presenting them concisely.

• Enhancing Human-AI Interactions: ChatGPT engages in interactive dialogues, making it highly useful for customer support, virtual assistants, and tasks requiring human-like conversation. It also translates text between languages, though it may not grasp idioms and cultural nuances. Despite this, it helps access and understand information that might otherwise be inaccessible due to language differences.

• Education/Training Tool: ChatGPT simplifies complex concepts, making them more understandable for users by providing clear explanations and breaking down technical jargon. It is an extremely useful educational tool that helps improve writing skills by offering valuable feedback, suggesting improvements, and providing practical examples. This capability also makes it ideal for marketers looking to refine their written content, like ad copy, email newsletters, or blog posts.

Limitations of ChatGPT

While ChatGPT was the first widely used AI chatbot and has been instrumental in the advancements of AI, it still has its limitations.

• Data Retrieval: ChatGPT is not designed for real-time data retrieval, so it is unable to provide the latest updates in its outputs. Its training data is current only up to a specific cutoff date, making it less useful for current events or timely questions. Since ChatGPT can’t access or retrieve live data, it generates responses using patterns learned during its training. Essentially, it makes educated guesses based on past data rather than providing real-time updates. This can lead to unpredictable or inaccurate results, making it less reliable.

• Consistency and Reliability: ChatGPT can provide different answers for the same question. This unpredictability stems from two main factors. First, the generative nature of the tool is designed to be creative and consider multiple ways to answer a query. Second, the datasets ChatGPT is trained on are often selected by a small group of decision-makers, which can lead to the use of large, uncurated data sources. For example, one of the largest datasets that LLMs and ChatGPT are trained on is Wikipedia. While Wikipedia does contain a large repository of information, Wikipedia’s contributor base is not very diverse. This lack of diversity can lead to biases in the data, which, in turn, might be reflected in the ChatGPT’s outputs.

• Reasoning and Logic: ChatGPT can produce responses that appear logical but lack genuine understanding and reasoning. Because these tools rely on patterns in the data they are trained on, it is incredibly common for them to provide incorrect or nonsensical answers. It’s also very common for ChatGPT to run into issues with invalid or faulty reasoning when constructing arguments. For example, ChatGPT might provide a persuasive argument using a “reasonable explanation” backed by “historical events.” However, the AI may reference events that never occurred or misinterpret facts. This becomes problematic when users accept these AI-generated responses at face value, leading to widespread misinformation.

While ChatGPT is an extremely powerful tool, it is only as powerful as the data it is trained on, making its responses not always reliable, not always accurate, and not always logical. These issues highlight the need for diverse data sources and greater transparency in LLM training processes.

What is Google’s Search Generative Experience (SGE)?

Google’s Search Generative Experience (SGE) enhances the search experience by combining Google’s search algorithm with LLMs. This integration involves various technologies, including structured data for quick context and content identification, real-time data analysis, and quality assurance processes to ensure up-to-date and accurate information.

Strengths of SGE

• Improved Search Experience: SGE was built with one purpose in mind: to provide direct, concise answers to user queries. Google does this within its own ecosystem, reducing the users’ need to visit external websites. SGE’s ability to summarize large volumes of information and identify key information in short summaries directly in search results allows for a more streamlined and efficient search experience.

• Enhanced User Interaction with Conversational Search: SGE integrates conversational elements into its search experience. Instead of relying solely on keyword-based queries, users can interact with the search engine in a more natural manner, allowing for more nuanced responses. This conversational approach helps SGE better understand the context of user questions and maintain coherence throughout a session. This means users can have a more cohesive and coherent search experience, with follow-up questions leading to more refined and relevant answers.

• Data Retrieval: SGE functions as an advanced information retrieval system that is designed to summarize and present information from Google’s extensive index of web content. Unlike ChatGPT, which relies on pre-existing training data and lacks real-time web access, SGE integrates live data with Google’s search algorithm and quality assurance processes. It pulls in real-time data from a wide range of sources, providing some of the most recent information available. It is important to note that the timeliness of this data depends on how frequently Google updates its index, which may cause minor delays.

Limitations of SGE

• Lack of Independent Reasoning: As previously mentioned, SGE is an information retrieval tool. It excels at summarizing and organizing existing content. It does not have the ability to generate original insights or ideas. It does not analyze information in a way that involves critical thinking or problem-solving. Its responses are solely based on existing web content and presenting that information.

• Consistency and Reliability: Despite being supported by Google’s advanced search algorithms, SGE can still produce inconsistent or inaccurate results. Since the information retrieved and summarized by SGE is drawn from a broad range of web content (both a pro and a con), it can pull information from both reliable and unreliable sources. While Google works to filter and rank sources to improve quality, some low-quality or biased information may still affect the results. Google is actively working to enhance and refine the SGE process to ensure high-quality and accurate information. The company has made it clear that they are investing heavily in this technology and have implemented it across 86% of all search queries.

ChatGPT and Google SGE: Key Differences and Features

Unlocking the Potential of ChatGPT and SGE

ChatGPT and SGE are fundamentally different tools with distinct purposes. When discussing AI optimization with clients, it’s essential to understand and communicate these differences. ChatGPT is a generative AI tool for creating and manipulating text based on user input. It can help brainstorm ideas, edit copy, and more. In contrast, SGE is designed to retrieve and present information clearly and concisely. It can help answer user queries by summarizing and organizing information from various sources.

By leveraging the unique strengths of both tools, you can develop effective digital strategies that meet a wide range of user needs and keep you ahead in the digital world. If you’re interested in learning more about these technologies or discussing how they can be integrated into your digital strategy, send us an email.