Speculations Swirl as Rumors of GPT-6 Leak Ignite Frenzy Among AI Enthusiasts

Theoretically, considering data communication and computation time, 15 pipelines are quite a lot. However, once KV cache and cost are added, if OpenAI mostly uses 40GB A100 GPUs, such an architecture is theoretically meaningful. However, the author states that he does not fully understand how OpenAI manages to avoid generating «bubbles» (huge bubbles) like the one shown in the figure below, given such high pipeline parallelism.

This timing is strategic, allowing the team to avoid the distractions of the American election cycle and to dedicate the necessary time for training and implementing safety measures. OpenAI is also working on enhancing real-time voice interactions, aiming to create a more natural and seamless experience for users. Increasing model size as a proxy for increasing performance was established in 2020 by Kaplan and others at OpenAI.

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Let’s talk about scale and scope for a minute, and specifically the parameter and token counts used in the training of the LLMs. These two together drive the use of flops and the increasingly emergent behavior of the models. Meta’s ability to squeeze more performance out of a particular model size isn’t all that’s changed since Llama 2’s release in June of 2023. The company’s consistent pace and relatively open license has encouraged an enthusiastic response from the broader tech industry. Intel and Qualcomm immediately announced support for Llama 3 on their respective hardware; AMD made an announcement a day later. Llama 3 also defeats competing small and midsize models, like Google Gemini and Mistral 7B, across a variety of benchmarks, including MMLU.

An example Zuckerberg offers is asking it to make a “killer margarita.” Another is one I gave him during an interview last year, when the earliest version of Meta AI wouldn’t tell me how to break up with someone. The first stage is pre-filling, where the prompt text is used to generate a KV cache and the logits (probability distribution of possible token outputs) for the first output. This stage is usually fast because the entire prompt text can be processed in parallel.

GPT-4 is believed to be such a smart program that it can deter the context in a far better manner compared to GPT-3.5. For example, when GPT-4 was asked about a picture and to explain what the joke was in it, it clearly demonstrated a full understanding of why a certain image appeared to be humorous. On the other hand, GPT-3.5 does not have an ability to interpret context in such a sophisticated manner. It can only do so on a basic level, and that too, with textual data only.

Altman could have been referring to GPT-4o, which was released a couple of months later. For example, ChatGPT-4 was released just three months after GPT-3.5. Therefore, it’s not unreasonable to expect GPT-5 to be released just months after GPT-4o. It’s been a few months since the release of ChatGPT-4o, the most capable version of ChatGPT yet.

GPT-3.5 was a significant step up from the base GPT-3 model and kickstarted ChatGPT. It will be able to perform tasks in languages other than English and will have a larger context window than Llama 2. A context window reflects the range of text that the LLM can process at the time the information is generated. This implies that the model will be able to handle larger chunks of text or data within a shorter period of time when it is asked to make predictions and generate responses.

And it has more “steerability,” meaning control over responses using a “personality” you pick—say, telling it to reply like Yoda, or a pirate, or whatever you can think of. It’s available via the ChatGPT Plus subscription for $20 a month and uses 1 trillion parameters, or pieces of information, to process queries. / A newsletter from Alex Heath about the tech industry’s inside conversation. For the visual ChatGPT model, OpenAI originally intended to train from scratch, but this approach is not mature enough, so they decided to start with text first to mitigate risks. Guessing decoding has two key advantages as a performance optimization target. Second, the advantages it provides are often orthogonal to other methods, as its performance comes from transforming sequential execution into parallel execution.

Apple reportedly plans to deploy its own models for on-device processing, touting that its operating system works in sync with its custom-designed silicon, which has been optimised for these AI features while preserving user privacy. For more advanced processing, Apple is in talks with Google to license Gemini as an extension of its deal to have Google Search as the default search engine on the iPhone operating system. Since the launch of ChatGPT a year ago, OpenAI has been advancing the capabilities of its large language models, deep-learning algorithms that are able to achieve general-purpose language understanding and generation. This article is part of a larger series on using large language models (LLMs) in practice.

«The UK risks falling behind»: NatWest AI Chief warns of tech startup «barriers»

You can use it through the OpenAI website as part of its ChatGPT Plus subscription. It’s $20 a month, but you’ll get priority access to ChatGPT as well, so it’s never too busy to have a chat. There are some ways to use GPT-4 for free, but those sources tend to have a limited number of questions, or don’t always use GPT-4 due to limited availability.

Upon its release, ChatGPT’s popularity skyrocketed literally overnight. It grew to host over 100 million users in its first two months, making it the most quickly-adopted piece of software ever made to date, though this record has since been beaten by the Twitter alternative, Threads. ChatGPT’s popularity dropped briefly in June 2023, reportedly losing 10% of global users, but has since continued to grow exponentially. If you’d like to maintain a history of your previous chats, sign up for a free account. Users can opt to connect their ChatGPT login with that of their Google-, Microsoft- or Apple-backed accounts as well. At the sign up screen, you’ll see some basic rules about ChatGPT, including potential errors in data, how OpenAI collects data, and how users can submit feedback.

It claims that much more in-depth safety and security audits need to be completed before any future language models can be developed. CEO Sam Altman has repeatedly said that he expects future GPT models to be incredibly disruptive to the way we live and work, so OpenAI wants to take more time and care with future releases. With that as context, let’s talk about the Inflection-1 foundation model.

Another key aspect we noticed in our testing was that GPT-3.5 as well as GPT-4 were making different types of errors when giving responses. While some of these errors were advanced and out of reach of the program, there were other basic errors as well, such as, wrong chemical formula, arithmetical errors, and numerous others as well. Our tech team got early access to GPT-4 and we were able to test both of them side by side.

US artificial intelligence leader OpenAI applies for GPT-6, GPT-7 trademarks in China

However, this does not scale well with large batch sizes or low alignment of the draft model. Intuitively, the probability of two models agreeing on consecutive long sequences decreases exponentially, which means that as the arithmetic intensity increases, the return on guessing decoding quickly diminishes. The basic idea behind guessing decoding is to use a smaller, faster draft model to pre-decode multiple tokens and then feed them as a batch to the oracle model.

• One of the key differences between GPT-3.5 and GPT-4 lies within reduced biases in the latter version.
• Once KV cache and overhead are added, theoretically, if most of OpenAI’s GPUs are 40GB A100s, this makes sense.
• GPT-4o mini will reportedly be multimodal like its big brother (which launched in May), with image inputs currently enabled in the API.
• Additionally, as the sequence length increases, the KV cache also becomes larger.
• More specifically, the architecture consisted of eight models, with each internal model made up of 220 billion parameters.

It functions due to its inherent flexibility to adapt to new circumstances. In addition, it will not deviate from its predetermined path in order to protect its integrity and foil any unauthorized commands. With the assistance of longer contexts, GPT-4 is able to process longer texts. [SPONSORED GUEST ARTICLE]  Meta (formerly Facebook) has a corporate culture of aggressive technology adoption, particularly in the area of AI and adoption of AI-related technologies, such as GPUs that drive AI workloads.

We know it will be “materially better” as Altman made that declaration more than once during interviews. This has been sparked by the success of Meta’s Llama 3 (with a bigger model coming in July) as well as a cryptic series of images shared by the AI lab showing the number 22. The image appeared to show either that it would be made up of 3.5 trillion parameters — almost twice as many as OpenAI’s current GPT-4 model — or between three and five trillion parameters, depending on how you view the blurry image. At the Semicon Taiwan conference today, Dr Jung Bae Lee reportedly got up on stage and showed the audience graphic revealing key details of ChatGPT 5 — a model that will reportedly be blessed with PhD-level intelligence. However, GPT-5 will have superior capabilities with different languages, making it possible for non-English speakers to communicate and interact with the system.

I think before we talk about a GPT-5-like model we have a lot of other important things to release first. «We will release an amazing model this year, I don’t know what we will call it,» he said. «I think before we talk about a GPT-5-like model we have a lot of other important things to release first.»

“I also agreed that as capabilities get more and more serious that the safety bar has got to increase. But unfortunately, I think the letter is missing most technical nuance about where we need to pause — an earlier version of the letter claimed we were training GPT-5. We are not and we won’t be for some time, so in that sense, it was sort of silly — but we are doing other things on top of GPT-4 that I think have all sorts of safety issues that are important to address and were totally left out of the letter. So I think moving with caution, and an increasing rigor for safety issues is really important. I don’t think the [suggestions in the] letter is the ultimate way to address it,” he said. He sees size as a false measurement of model quality and compares it to the chip speed races we used to see.

But the Bard launch will only allow people to use text prompts as of today, with the company promising to allow audio and image interaction “in coming months”. Additionally, GPT-3.5’s training data encompassed various sources, such as books, articles, and websites, to capture a diverse range of human knowledge and language. By incorporating multiple sources, GPT-3.5 aimed to better understand context, semantics, and nuances in text generation. GPT-3 was brute-force trained in most of the Internet’s available text data. And users could communicate with it in plain natural language; GPT-3 would receive the description and recognize the task it had to do. IOS 18 is expected to feature numerous LLM-based generative AI capabilities.

On that note, it’s unclear whether OpenAI can raise the base subscription for ChatGPT Plus. I’d say it’s impossible right now, considering that Google also charges $20 a month for Gemini Advanced, which also gets you 2TB of cloud storage. Moreover, Google offers Pixel 9 buyers a free year of Gemini Advanced access. You could give ChatGPT with GPT-5 your dietary requirements, access to your smart fridge camera and your grocery store account and it could automatically order refills without you having to be involved.

In contrast to conventional reinforcement learning, GPT-3.5’s capabilities are somewhat restricted. To anticipate the next word in a phrase based on context, the model engages in “unsupervised learning,” where it is exposed to a huge quantity of text data. With the addition of improved reinforcement learning in GPT-4, the system is better able to learn from the behaviors and preferences of its users.

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AI tools, including the most powerful versions of ChatGPT, still have a tendency to hallucinate. They can get facts incorrect and even invent things seemingly out of thin air, especially when working in languages other ChatGPT App than English. With additional training data at its disposal, GPT-4 is more natural and precise in conversation. This is because of progress made in the areas of data collecting, cleansing, and pre-processing.

If you are not familiar with MoE, please read our article from six months ago about the general GPT-4 architecture and training costs. Additionally, we will outline the cost of training and inferring GPT-4 on A100, as well as how it scales with H100 in the next generation model architecture. The basis for the summer release rumors seems to come from third-party companies given early access to the new OpenAI model. These enterprise customers of OpenAI are part of the company’s bread and butter, bringing in significant revenue to cover growing costs of running ever larger models. The mid-range Pro version of Gemini beats some other models, such as OpenAI’s GPT3.5, but the more powerful Ultra exceeds the capability of all existing AI models, Google claims.

It is very likely that OpenAI has successfully borne the cost of these bubbles. In each forward propagation inference (generating one token), GPT-4 only needs to use about 280 billion parameters and 560 TFLOPs. In comparison, a pure dense model requires about 18 trillion parameters and approximately 3,700 TFLOPs of computation for each forward propagation. The article points out that GPT-4 has a total of 18 trillion parameters in 120 layers, while GPT-3 has only about 175 billion parameters. In other words, the scale of GPT-4 is more than 10 times that of GPT-3.

Based on these responses, one can rightfully conclude that the technologies are still not mature enough. It also opens up the possibility that when a program can make such a basic error, how can this technology be used for the larger context i the long run. This is due to the fact that input tokens (prompts) have a different cost than completion tokens (answers).

PCMag.com is a leading authority on technology, delivering lab-based, independent reviews of the latest products and services. You can foun additiona information about ai customer service and artificial intelligence and NLP. Our expert industry analysis and practical solutions help you make better buying decisions and get more from technology. OpenAI admits that ChatGPT-4 still struggles with bias; it could even deliver hate speech (again).

There are also about 550 billion parameters in the model, which are used for attention mechanisms. Altman has said it will be much more intelligent than previous models. «I am excited about it being smarter,» said Altman in his interview with Fridman. gpt 5 parameters Red teaming is where the model is put to extremes and tested for safety issues. The next stage after red teaming is fine-tuning the model, correcting issues flagged during testing and adding guardrails to make it ready for public release.

For instance, users will be able to ask it to describe an image, making it even more accessible to people with visual impairments. A larger number of datasets will be needed for model training if more parameters are included in the model. That seems to imply that GPT-3.5 was trained using a large number of different datasets (almost the whole Wikipedia). There is an option called “context length” that specifies the maximum number of tokens that may be utilized in a single API request. The maximum token amount for a request was initially set at 2,049 in the 2020 release of the original GPT-3.5 devices.

In fact, we expect companies like Google, Meta, Anthropic, Inflection, Character, Tencent, ByteDance, Baidu, and others to have models with the same or even greater capabilities as GPT-4 in the short term. The basic principle of «speculative decoding» is to use a smaller, faster draft model to decode multiple tokens in advance, and then input them as a batch into the prediction model. If OpenAI uses speculative decoding, they may only use it in sequences of about 4 tokens.

Conversational AI revolutionizes the customer experience landscape

Normalising harmful sexual behaviours such as rape, sadism or paedophilia is bad news for society. An ABC investigation revealed the use of generative AI to create fake influencers by manipulating women’s social media images is already widespread. Much of this content depicts unattainable body ideals, and some depicts people who appear to be at best barely of consenting age.

However, it is essential to balance AI and human involvement and critically evaluate the information provided by ChatGPT. By harnessing AI’s power while embracing human educators’ invaluable role, we can create a learning environment that maximizes student engagement and fosters meaningful learning outcomes. Based on the selected articles, we categorized the factors previously discussed and presented them in Table 3. Table 3 summarizes the main points discussed in the paragraph, highlighting the factors influencing student engagement and learning outcomes when using ChatGPT in education. When it comes to developing and implementing conversational chatbots for customer service, Netguru provides comprehensive services including discovery, strategy, design, development, integration, testing, deployment, and maintenance.

The following table compares some key features of Google Gemini and OpenAI products. However, in late February 2024, Gemini’s image generation feature was halted to undergo retooling after generated images were shown to depict factual inaccuracies. Google intends to improve the feature so that Gemini can remain multimodal in the long run. Some believe rebranding the platform as Gemini might have been done ChatGPT App to draw attention away from the Bard moniker and the criticism the chatbot faced when it was first released. All conversation data collected is anonymized and complies with current privacy practices and regulations. If you would like to learn more about how your information is collected and used, read the WHO’s privacy policy, Soul Machines Privacy Policy, OpenAI Privacy Policy, OpenAI Terms of Use.

The organization’s Dynamic Automation Platform is built on multiple LLMs, to help organizations build highly bespoke and unique human-like experiences. By 2028, experts predict the conversational AI market will be worth an incredible $29.8 billion. The rise of new solutions, like generative AI and large language models, even means the tools available from vendors today are can you more advanced and powerful than ever. As knowledge bases expand, conversational AI will be capable of expert-level dialogue on virtually any topic.

3 Study selection

This means that the speech recognition technology needs to be as accurate as possible. Every word matters, as missing or changing even a single word in a sentence can completely change its meaning. However, speech recognition generative ai and conversational ai technology often has difficulty understanding different languages or accents, not to mention dealing with background noise and cross-conversations, so finding an accurate speech-to-text model is essential.

Research shows that the size of language models (number of parameters), as well as the amount of data and computing power used for training all contribute to improved model performance. In contrast, the architecture of the neural network powering the model seems to have minimal impact. To leverage the exciting innovations and benefits of conversational AI in the contact center, you’ll need a scalable, reliable CCaaS platform that provides carrier grade voice quality and full omnichannel capabilities with the flexibility to customize. Conversational AI can also be integrated into existing systems to enhance your current offer. AI has been gaining importance in the contact center – from the first flush of IVR to today’s ecosystem including ML, NLU, natural language processing (NLP), automatic speech recognition (ASR), text-to-speech (TTS), and speech-to-text (STT) processing.

This guide is your go-to manual for generative AI, covering its benefits, limits, use cases, prospects and much more.

Wong said he’s most excited about large language models’ ability to have longer context windows, enabling them to keep more information in their short-term memory and answer ever-more complex questions. GALE supports both long-term and short-term applications, enabling businesses to quickly develop temporary solutions like email services or outreach campaigns. Users will also have access to the company’s Agent AI tool, which provides real-time guidance, automated summaries, coaching, and AI-driven playbooks to support agents.

Virtual assistants, chatbots and more can understand context and intent and generate intelligent responses. The future will bring more empathetic, knowledgeable and immersive conversational AI experiences. The emergence of tools like ChatGPT has transformed conversational interactions between humans and machines. Generative AI’s ability to provide accurate and contextually relevant responses makes it particularly valuable for automated customer service environments, such as chatbots and virtual avatars.

For instance, they can use tools, like information stores or knowledge bases to surface information, and plan and execute tasks. With advanced algorithms and machine learning, the agents can adapt to new situations and evolve over time, becoming more efficient. They offered valuable insights into how generative AI solutions worked and how powerful they could be. However, many companies struggled to take advantage of LLMs due to the computing power and data required.

• Tech companies already spend a lot of time and money cleaning and filtering the data they scrape, with one industry insider recently sharing they sometimes discard as much as 90% of the data they initially collect for training models.
• Gemini models used by Conversational Agents and Agent Assist products can be grounded in information from an organization’s own resources to increase accuracy in the responses generated.
• We leverage industry-leading tools and technologies to build custom solutions that are tailored to each business’s specific needs.
• Addressing these challenges requires collaborative efforts from researchers across various disciplines, including AI, ethics, psychology, linguistics, and more.
• By actively monitoring its performance, institutions can identify and address issues, refine the system, and enhance the overall user experience.

In May 2024, Google announced further advancements to Google 1.5 Pro at the Google I/O conference. Upgrades include performance improvements in translation, coding and reasoning features. The upgraded Google 1.5 Pro also has improved image and video understanding, including the ability to directly process voice inputs using native audio understanding. The model’s context window was increased to 1 million tokens, enabling it to remember much more information when responding to prompts. Bard also integrated with several Google apps and services, including YouTube, Maps, Hotels, Flights, Gmail, Docs and Drive, enabling users to apply the AI tool to their personal content. In January 2023, Microsoft signed a deal reportedly worth $10 billion with OpenAI to license and incorporate ChatGPT into its Bing search engine to provide more conversational search results, similar to Google Bard at the time.

1 Benefits and challenges of using ChatGPT in education

You can foun additiona information about ai customer service and artificial intelligence and NLP. Moving on to the third RQ, Deploying AI chatbots in education demands an ethical framework with content guidelines, preventing misinformation. Teacher supervision ensures accuracy, while training raises AI awareness and tackles biases. Privacy and data protection are paramount, and regular monitoring addresses ethical concerns.

We started this study with the goal of examining the current state and future directions of conversational AI in software engineering. Using a rapid review approach guided by the PRISMA method, we selected 183 relevant peer-reviewed articles. They assist students in personalized learning with ChatGPT, fostering critical thinking and understanding. Educators monitor usage, offer feedback, and address ethical ChatGPT considerations, promoting digital literacy. Thoughtful integration creates engaging and personalized learning environments, empowering students and enhancing the overall educational experience. Despite its benefits, challenges with ChatGPT include biases in AI models, the need for accuracy in responses, lack of emotional intelligence, and the absence of critical thinking abilities (Ahn, 2023).

Our collective line of inquiry needs to shift towards exploring a state of interdependence, where society can maximize the benefits of these tools while maintaining human autonomy and creativity. As hiring managers receive an increasing number of AI-generated applications, they are finding it difficult to uncover the true capabilities and motivations of candidates, which is resulting in less-informed hiring decisions. Generative AI tools can produce cover letters based on job descriptions and resumes, but they often lack the personal touch and genuine passion that human-crafted letters might convey. For recipients, the polished nature of AI-generated content might lead to a surface-level engagement without deeper consideration. This superficial engagement could result in the undermining of the quality of communication and the authenticity of human connections. When individuals process information through the central route, they engage in thoughtful and critical evaluation of information.

Many generative AI companies are currently facing copyright infringement lawsuits over their use of training data, and their defences are likely to rely on claiming fair use. As with other problematic behaviours where the issue lies more with providers than users, it’s time to hold sexbot providers accountable. Research has shown that sexual roleplaying is one of the most common uses of ChatGPT, and millions of people interact with AI-powered systems designed as virtual companions, such as such as Character.AI, Replika, and Chai.AI. Developing an enterprise-ready application that is based on machine learning requires multiple types of developers. These supporting services need not exist in the Orchestrator’s local environment (e.g., in the same Kubernetes cluster). In fact, these services will often be located in locations other than the Orchestrator’s due to concerns around data sensitivity, regulatory compliance, or partner business constraints.

For instance, the clothing company Chubbies Inc. opted to create a young and hip-sounding agent with the slightly sarcastic name Duncan Smothers. Meanwhile, some other brands have opted for AI agents with British accents and a more serious tone. Google introduces a new conversational AI experience within search Ads, using its advanced Gemini AI model. Advertisers can now generate ad content automatically, pulling creative elements and keywords directly from a website URL. This technology streamlines the ad creation process, providing advertisers with a more intuitive and efficient approach to generating ad content. More educated workers benefit while less-educated workers are displaced through automation – a trend known as “skill-biased technological change”.

The ChatGPT features include integrated writing tools, image cleanup, article summaries, and a typing input for the redesigned Siri experience. By leveraging IKEA’s product database, the AssistBot has an exceptional understanding of the company’s catalog, surpassing that of a human assistant. Rather than leaving customers to navigate the complexities of tags, categories, and collections on their own, the AssistBot will offer guidance throughout the process. Chatbots can handle password reset requests from customers by verifying their identity using various authentication methods, such as email verification, phone number verification, or security questions. The chatbot can then initiate the password reset process and guide customers through the necessary steps to create a new password. The AI powered chatbots can also provide a summary of the order and request confirmation from the customer.

What are the concerns about Gemini?

The weighted edges indicate the number of collaborations between authors, i.e., the thicker the edges, the more the two authors collaborated. To further understand collaboration communities, we analyzed the co-author networks, where each node is an author, and each edge between two nodes indicates a collaboration on a paper. Figure 4 presents the resulting network, laid out according to the Force Atlas 2 algorithm in the Gephi Visualization Software (Bastian et al., 2009). Key authors, or those central to the network with many connections, can be seen as larger nodes, often positioned toward the center of the network clusters. The figure shows a number of key authors (larger nodes) and a few large communities of collaborations but a significant number of smaller, isolated groups or pairs of collaborators, indicating a growing interest in the area.

AI has ushered in a new era of human-computer collaboration as businesses embrace this technology to improve processes and efficiency. From the perspective of the application consumer, this is a transformative change in user experience. The complexity, as measured by time and human effort, is greatly reduced while simultaneously improving the quality of the outcome relative to what a human would typically achieve. Note this is not just a theoretical possibility—in our conversations with CTOs and CIOs across the world, enterprises are already planning to roll out applications following this pattern in the next 12 months. In fact, Microsoft recently announced a conversational AI app specifically targeting travel use cases.

• Amid the emergence of generative AI — which can generate text, images, and video — it’s a good time to be cautious amid the hype, especially given negative developments at Super Micro Computer (SMCI).
• —Answers vary from paper to paper and may include software development, software testing, and requirements engineering, among others.
• Text-generating AI models like ChatGPT have a tendency to regurgitate content from their training data.
• Generative AI lets users create new content — such as animation, text, images and sounds — using machine learning algorithms and the data the technology is trained on.
• The problem is, as hundreds of millions are aware from their stilted discourse with Alexa, the assistant was not built for, and has never been primarily used for, back-and-forth conversations.

Therefore, it is crucial to validate and verify the information provided by ChatGPT through reputable sources and critical analysis. Overall, improved access to information is a significant advantage of ChatGPT, as it simplifies retrieving data and enables users to obtain relevant answers more efficiently. Whether developing or engaging with AI — in cybersecurity or any other context — it’s essential to keep humans in the loop throughout the process. Training data must be regularly audited by diverse and inclusive teams and refined to reduce bias and misinformation. While people themselves are prone to the same problems, continuous supervision and the ability to explain how AI draws the conclusions it does can greatly mitigate these risks. That’s why it’s imperative that biometric systems are kept under maximum security and backed up with responsible data retention policies.

At the end of July, the company introduced XO Express, a new conversational AI platform tailored for smaller businesses. Overall, the former employees paint a picture of a company desperately behind its Big Tech rivals Google, Microsoft, and Meta in the race to launch AI chatbots and agents, and floundering in its efforts to catch up. In doing so, they can choose from 30+ LLMs, including community, open-source, and finetuned models. Moreover, the vendor allows users to apply different models to different apps to optimize their performance. The application also features Agent Assist capabilities to improve employee productivity. Gemini models used by Conversational Agents and Agent Assist products can be grounded in information from an organization’s own resources to increase accuracy in the responses generated.