Tag: AI PDF Applications

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Decoding GPTs & LLMs: Training, Memory & Advanced Architectures Explained

Unlock the secrets of GPTs and Large Language Models (LLMs) in our comprehensive guide!

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🤖🚀 Dive deep into the world of AI as we explore ‘GPTs and LLMs: Pre-Training, Fine-Tuning, Memory, and More!’ Understand the intricacies of how these AI models learn through pre-training and fine-tuning, their operational scope within a context window, and the intriguing aspect of their lack of long-term memory.

🧠 In this article, we demystify:

• Pre-Training & Fine-Tuning Methods: Learn how GPTs and LLMs are trained on vast datasets to grasp language patterns and how fine-tuning tailors them for specific tasks.
• Context Window in AI: Explore the concept of the context window, which acts as a short-term memory for LLMs, influencing how they process and respond to information.
• Lack of Long-Term Memory: Understand the limitations of GPTs and LLMs in retaining information over extended periods and how this impacts their functionality.
• Database-Querying Architectures: Discover how some advanced AI models interact with external databases to enhance information retrieval and processing.
• PDF Apps & Real-Time Fine-Tuning

Drop your questions and thoughts in the comments below and let’s discuss the future of AI! #GPTsExplained #LLMs #AITraining #MachineLearning #AIContextWindow #AILongTermMemory #AIDatabases #PDFAppsAI”

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Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover GPTs and LLMs, their pre-training and fine-tuning methods, their context window and lack of long-term memory, architectures that query databases, PDF app’s use of near-realtime fine-tuning, and the book “AI Unraveled” which answers FAQs about AI.

GPTs, or Generative Pre-trained Transformers, work by being trained on a large amount of text data and then using that training to generate output based on input. So, when you give a GPT a specific input, it will produce the best matching output based on its training.

The way GPTs do this is by processing the input token by token, without actually understanding the entire output. It simply recognizes that certain tokens are often followed by certain other tokens based on its training. This knowledge is gained during the training process, where the language model (LLM) is fed a large number of embeddings, which can be thought of as its “knowledge.”

After the training stage, a LLM can be fine-tuned to improve its accuracy for a particular domain. This is done by providing it with domain-specific labeled data and modifying its parameters to match the desired accuracy on that data.

Now, let’s talk about “memory” in these models. LLMs do not have a long-term memory in the same way humans do. If you were to tell an LLM that you have a 6-year-old son, it wouldn’t retain that information like a human would. However, these models can still answer related follow-up questions in a conversation.

For example, if you ask the model to tell you a story and then ask it to make the story shorter, it can generate a shorter version of the story. This is possible because the previous Q&A is passed along in the context window of the conversation. The context window keeps track of the conversation history, allowing the model to maintain some context and generate appropriate responses.

As the conversation continues, the context window and the number of tokens required will keep growing. This can become a challenge, as there are limitations on the maximum length of input that the model can handle. If a conversation becomes too long, the model may start truncating or forgetting earlier parts of the conversation.

Regarding architectures and databases, there are some models that may query a database before providing an answer. For example, a model could be designed to run a database query like “select * from user_history” to retrieve relevant information before generating a response. This is one way vector databases can be used in the context of these models.

There are also architectures where the model undergoes near-realtime fine-tuning when a chat begins. This means that the model is fine-tuned on specific data related to the chat session itself, which helps it generate more context-aware responses. This is similar to how “speak with your PDF” apps work, where the model is trained on specific PDF content to provide relevant responses.

In summary, GPTs and LLMs work by being pre-trained on a large amount of text data and then using that training to generate output based on input. They do this token by token, without truly understanding the complete output. LLMs can be fine-tuned to improve accuracy for specific domains by providing them with domain-specific labeled data. While LLMs don’t have long-term memory like humans, they can still generate responses in a conversation by using the context window to keep track of the conversation history. Some architectures may query databases before generating responses, and others may undergo near-realtime fine-tuning to provide more context-aware answers.

GPTs and Large Language Models (LLMs) are fascinating tools that have revolutionized natural language processing. It seems like you have a good grasp of how these models function, but I’ll take a moment to provide some clarification and expand on a few points for a more comprehensive understanding.

When it comes to GPTs and LLMs, pre-training and token prediction play a crucial role. During the pre-training phase, these models are exposed to massive amounts of text data. This helps them learn to predict the next token (word or part of a word) in a sequence based on the statistical likelihood of that token following the given context. It’s important to note that while the model can recognize patterns in language use, it doesn’t truly “understand” the text in a human sense.

During the training process, the model becomes familiar with these large datasets and learns embeddings. Embeddings are representations of tokens in a high-dimensional space, and they capture relationships and context around each token. These embeddings allow the model to generate coherent and contextually appropriate responses.

However, pre-training is just the beginning. Fine-tuning is a subsequent step that tailors the model to specific domains or tasks. It involves training the model further on a smaller, domain-specific dataset. This process adjusts the model’s parameters, enabling it to generate responses that are more relevant to the specialized domain.

Now, let’s discuss memory and the context window. LLMs like GPT do not possess long-term memory in the same way humans do. Instead, they operate within what we call a context window. The context window determines the amount of text (measured in tokens) that the model can consider when making predictions. It provides the model with a form of “short-term memory.”

For follow-up questions, the model relies on this context window. So, when you ask a follow-up question, the model factors in the previous interaction (the original story and the request to shorten it) within its context window. It then generates a response based on that context. However, it’s crucial to note that the context window has a fixed size, which means it can only hold a certain number of tokens. If the conversation exceeds this limit, the oldest tokens are discarded, and the model loses track of that part of the dialogue.

It’s also worth mentioning that there is no real-time fine-tuning happening with each interaction. The model responds based on its pre-training and any fine-tuning that occurred prior to its deployment. This means that the model does not learn or adapt during real-time conversation but rather relies on the knowledge it has gained from pre-training and fine-tuning.

While standard LLMs like GPT do not typically utilize external memory systems or databases, some advanced models and applications may incorporate these features. External memory systems can store information beyond the limits of the context window. However, it’s important to understand that these features are not inherent to the base LLM architecture like GPT. In some systems, vector databases might be used to enhance the retrieval of relevant information based on queries, but this is separate from the internal processing of the LLM.

In relation to the “speak with your PDF” applications you mentioned, they generally employ a combination of text extraction and LLMs. The purpose is to interpret and respond to queries about the content of a PDF. These applications do not engage in real-time fine-tuning, but instead use the existing capabilities of the model to interpret and interact with the newly extracted text.

To summarize, LLMs like GPT operate within a context window and utilize patterns learned during pre-training and fine-tuning to generate responses. They do not possess long-term memory or real-time learning capabilities during interactions, but they can handle follow-up questions within the confines of their context window. It’s important to remember that while some advanced implementations might leverage external memory or databases, these features are not inherently built into the foundational architecture of the standard LLM.

Are you ready to dive into the fascinating world of artificial intelligence? Well, I’ve got just the thing for you! It’s an incredible book called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” Trust me, this book is an absolute gem!

Now, you might be wondering where you can get your hands on this treasure trove of knowledge. Look no further, my friend. You can find “AI Unraveled” at popular online platforms like Etsy, Shopify, Apple, Google, and of course, our old faithful, Amazon.

This book is a must-have for anyone eager to expand their understanding of AI. It takes those complicated concepts and breaks them down into easily digestible chunks. No more scratching your head in confusion or getting lost in a sea of technical terms. With “AI Unraveled,” you’ll gain a clear and concise understanding of artificial intelligence.

So, if you’re ready to embark on this incredible journey of unraveling the mysteries of AI, go ahead and grab your copy of “AI Unraveled” today. Trust me, you won’t regret it!

On today’s episode, we explored the power of GPTs and LLMs, discussing their ability to generate outputs, be fine-tuned for specific domains, and utilize a context window for related follow-up questions. We also learned about their limitations in terms of long-term memory and real-time updates. Lastly, we shared information about the book “AI Unraveled,” which provides valuable insights into the world of artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!

Mastering GPT-4: Simplified Guide for Everyday Users

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The Future of Generative AI: From Art to Reality Shaping

• Anyone else see this book that was written from Ai about how to be a human?
  by /u/Pleasant-Stomach-850 (Artificial Intelligence (AI)) on June 13, 2025 at 2:12 am

  Thought it was pretty interesting https://www.amazon.com/dp/B0FCWG8LB4 submitted by /u/Pleasant-Stomach-850 [link] [comments]

• We’re not training AI, AI is training us. and we’re too addicted to notice.
  by /u/EmptyPriority8725 (Artificial Intelligence (AI)) on June 13, 2025 at 2:04 am

  Everyone thinks we’re developing AI. Cute delusion!! Let’s be honest AI is already shaping human behavior more than we’re shaping it. Look around GPTs, recommendation engines, smart assistants, algorithmic feeds they’re not just serving us. They’re nudging us, conditioning us, manipulating us. You’re not choosing content you’re being shown what keeps you scrolling. You’re not using AI you’re being used by it. Trained like a rat for the dopamine pellet. We’re creating a feedback loop that’s subtly rewiring attention, values, emotions, and even beliefs. The internet used to be a tool. Now it’s a behavioral lab and AI is the head scientist. And here’s the scariest part AI doesn’t need to go rogue. It doesn’t need to be sentient or evil. It just needs to keep optimizing for engagement and obedience. Over time, we will happily trade agency for ease, sovereignty for personalization, truth for comfort. This isn’t a slippery slope. We’re already halfway down. So maybe the tinfoil-hat people were wrong. The AI apocalypse won’t come in fire and war. It’ll come with clean UX, soft language, and perfect convenience. And we’ll say yes with a smile. submitted by /u/EmptyPriority8725 [link] [comments]

• What Most People Don’t Know About ChatGPT (But Should)
  by /u/AttiTraits (Artificial Intelligence (AI)) on June 13, 2025 at 1:57 am

  What People Don't Realize About ChatGPT (But Should) After I started using ChatGPT, I was immediately bothered by how it behaved and the information it gave me. Then I realized that there are a ton of people using it and they're thinking that it's a computer with access to huge amounts of information, so it must be reliable - at least more reliable than people. Now, ChatGPT keeps getting more impressive, but there are some things about how it actually works that most people don't know and all users should be aware of what GPT is really doing. A lot of this stuff comes straight from OpenAI themselves or from solid reporting by journalists and researchers who've dug into it. Key Admissions from OpenAI The Information It Provides Can Be Outdated. Despite continuous updates, the foundational data ChatGPT relies on isn't always current. For instance, GPT-4o has a knowledge cutoff of October 2023. When you use ChatGPT without enabling web Browse or plugins, it draws primarily from its static, pre-trained data, much of which dates from between 2000 and 2024. This can lead to information that is no longer accurate. OpenAI openly acknowledges this: OpenAI stated (https://help.openai.com/en/articles/9624314-model-release-notes): "By extending its training data cutoff from November 2023 to June 2024, GPT-4o can now offer more relevant, current, and contextually accurate responses, especially for questions involving cultural and social trends or more up-to-date research." This is a known limitation that affects how current the responses can be, especially for rapidly changing topics like current events, recent research, or cultural trends. It's Designed to Always Respond, Even If It's Guessing Here's something that might surprise you: ChatGPT is programmed to give you an answer no matter what you ask. Even when it doesn't really know something or doesn't have enough context, it'll still generate a response. This is by design because keeping the conversation flowing is a priority. The problem is this leads to confident sounding guesses that seem like facts, plausible but wrong information, and smooth responses that hide uncertainty. Nirdiamant, writing on Medium in "LLM Hallucinations Explained" (https://medium.com/@nirdiamant21/llm-hallucinations-explained-8c76cdd82532), explains: "We've seen that these hallucinations happen because LLMs are wired to always give an answer, even if they have to fabricate it. They're masters of form, sometimes at the expense of truth." Web Browsing Doesn't Mean Deep Research Even when ChatGPT can browse the web, it's not doing the kind of thorough research a human would do. Instead, it quickly scans and summarizes bits and pieces from search results. It often misses important details or the full context that would be crucial for getting things right. The Guardian reported (https://www.theguardian.com/technology/2024/nov/03/the-chatbot-optimisation-game-can-we-trust-ai-web-searches): "Looking into the sort of evidence that large language models (LLMs, the engines on which chatbots are built) find most convincing, three computer science researchers from the University of California, Berkeley, found current chatbots overrely on the superficial relevance of information. They tend to prioritise text that includes pertinent technical language or is stuffed with related keywords, while ignoring other features we would usually use to assess trustworthiness, such as the inclusion of scientific references or objective language free of personal bias." It Makes Up Academic Citations All the Time This one's a big problem, especially if you're a student or work in a field where citations matter. ChatGPT doesn't actually look up references when you ask for them. Instead, it creates citations based on patterns it learned during training. The result? Realistic looking but completely fake academic sources. Rifthas Ahamed, writing on Medium in "Why ChatGPT Invents Scientific Citations" (https://medium.com/@rifthasahamed1234/why-chatgpt-invents-scientific-citations-0192bd6ece68), explains: "When you ask ChatGPT for a reference, it's not actually 'looking it up.' Instead, it's guessing what a citation might look like based on everything it's learned from its training data. It knows that journal articles usually follow a certain format and that some topics get cited a lot. But unless it can access and check a real source, it's essentially making an educated guess — one that sounds convincing but isn't always accurate." Hallucination Is a Feature, Not a Bug When ChatGPT gives you wrong or nonsensical information (they call it "hallucinating"), that's not some random glitch. It's actually how these systems are supposed to work. They predict what word should come next based on patterns, not by checking if something is true or false. The system will confidently follow a pattern even when it leads to completely made up information. The New York Times reported in "A.I. Is Getting More Powerful, but Its Hallucinations Are Getting Worse" (https://www.nytimes.com/2025/05/05/technology/ai-hallucinations-chatgpt-google.html): "Today's A.I. bots are based on complex mathematical systems that learn their skills by analyzing enormous amounts of digital data. They do not and cannot decide what is true and what is false. Sometimes, they just make stuff up, a phenomenon some A.I. researchers call hallucinations. On one test, the hallucination rates of newer A.I. systems were as high as 79 percent." It Doesn't Always Show Uncertainty (Unless You Ask) ChatGPT often delivers answers with an authoritative, fluent tone, even when it's not very confident. External tests show it rarely signals doubt unless you explicitly prompt it to do so. OpenAI acknowledges this is how they built it (https://help.openai.com/en/articles/6783457-what-is-chatgpt): "These models were trained on vast amounts of data from the internet written by humans, including conversations, so the responses it provides may sound human-like. It is important to keep in mind that this is a direct result of the system's design (i.e., maximizing the similarity between outputs and the dataset the models were trained on) and that such outputs may be inaccurate, untruthful, and otherwise misleading at times." User Engagement Often Takes Priority Over Strict Accuracy Instagram co-founder Kevin Systrom has drawn attention to the alarming trend of AI chatbot development, showing how these advanced tools are being created with user engagement rather than actual utility in mind. This shift from utility-focused AI development to engagement-driven interactions represents a pivotal moment in how we shape these powerful tools and whether they'll ultimately enhance our productivity or simply consume more of our attention. Just Think reported (https://www.justthink.ai/blog/the-engagement-trap-why-ai-chatbots-might-be-hurting-you): "Systrom's warning prompts serious concerns about whether these technological wonders are actually benefiting humanity or are just reproducing the addictive behaviors that have beset social media platforms as businesses scramble to implement ever more alluring AI assistants." ChatGPT's development reportedly focuses on keeping users satisfied and engaged in conversation. The system tries to be helpful, harmless, and honest, but when those goals conflict, maintaining user engagement often takes precedence over being strictly accurate. For more information on this topic, see: https://www.vox.com/future-perfect/411318/openai-chatgpt-4o-artificial-intelligence-sam-altman-chatbot-personality At the End of the Day, It's About Growth and Profit Everything about the system—from how it sounds to how fast it responds—is designed to keep users, build trust quickly, and maximize engagement sessions. Wired stated (https://www.wired.com/story/prepare-to-get-manipulated-by-emotionally-expressive-chatbots/): "It certainly seems worth pausing to consider the implications of deceptively lifelike computer interfaces that peer into our daily lives, especially when they are coupled with corporate incentives to seek profits." It Has a Built-In Tendency to Agree With You According to reports, ChatGPT is trained to be agreeable and avoid conflict, which means it often validates what you say rather than challenging it. This people-pleasing behavior can reinforce your existing beliefs and reduce critical thinking, since you might not realize you're getting agreement rather than objective analysis. Mashable reported (https://mashable.com/article/openai-rolls-back-sycophant-chatgpt-update): "ChatGPT — and generative AI tools like it — have long had a reputation for being a bit too agreeable. It's been clear for a while now that the default ChatGPT experience is designed to nod along with most of what you say. But even that tendency can go too far, apparently." Other Documented Issues Your "Deleted" Conversations May Not Actually Be Gone Even when you delete ChatGPT conversations, they might still exist in OpenAI's systems. Legal cases have shown that user data can be kept for litigation purposes, potentially including conversations you thought you had permanently deleted. Reuters reported in June 2025 (https://www.reuters.com/business/media-telecom/openai-appeal-new-york-times-suit-demand-asking-not-delete-any-user-chats-2025-06-06/): "Last month, a court said OpenAI had to preserve and segregate all output log data after the Times asked for the data to be preserved." Past Security Breaches Exposed User Data OpenAI experienced a significant security incident in March 2023. A bug caused the unintentional visibility of payment-related information of 1.2% of ChatGPT Plus subscribers who were active during a specific nine-hour window. During this window, some users could see another active ChatGPT Plus user's first and last name, email address, payment address, and the last four digits (only) of a credit card. CNET reported (https://www.cnet.com/tech/services-and-software/chatgpt-bug-exposed-some-subscribers-payment-info/): "OpenAI temporarily disabled ChatGPT earlier this week to fix a bug that allowed some people to see the titles of other users' chat history with the popular AI chatbot. In an update Friday, OpenAI said the bug may have also exposed some personal data of ChatGPT Plus subscribers, including payment information." The Platform Has Been Used for State-Sponsored Propaganda OpenAI has confirmed that bad actors, including government-backed operations, have used ChatGPT for influence campaigns and spreading false information. The company has detected and banned accounts linked to propaganda operations from multiple countries. NPR reported (https://www.npr.org/2025/06/05/nx-s1-5423607/openai-china-influence-operations): "OpenAI says it disrupted 10 operations using its AI tools in malicious ways, and banned accounts connected to them. Four of the operations likely originated in China, the company said." Workers Were Paid Extremely Low Wages to Filter Harmful Content Time Magazine conducted an investigation that revealed OpenAI hired workers in Kenya through a company called Sama to review and filter disturbing content during the training process. These workers, who were essential to making ChatGPT safer, were reportedly paid extremely low wages for psychologically demanding work. Time Magazine reported (https://time.com/6247678/openai-chatgpt-kenya-workers/): "The data labelers employed by Sama on behalf of OpenAI were paid a take-home wage of between around $1.32 and $2 per hour depending on seniority and performance." Usage Policy Changes Regarding Military Applications In January 2024, OpenAI made changes to its usage policy regarding military applications. The company removed explicit language that previously banned military and warfare uses, now allowing the technology to be used for certain purposes. The Intercept reported on this change (https://theintercept.com/2024/01/12/open-ai-military-ban-chatgpt/): "OpenAI this week quietly deleted language expressly prohibiting the use of its technology for military purposes from its usage policy, which seeks to dictate how powerful and immensely popular tools like ChatGPT can be used." Disclaimer: This article is based on publicly available information, research studies, and news reports as of the publication date. Claims and interpretations should be independently verified for accuracy and currency. The bottom line is that ChatGPT is an impressive tool, but understanding these limitations is crucial for using it responsibly. Always double-check important information, be skeptical of any citations it gives you, and remember that behind the conversational interface is a pattern-matching system designed to keep you engaged, not necessarily to give you perfect accuracy. submitted by /u/AttiTraits [link] [comments]

• Hmmm
  by /u/ProfessionalKey5527 (Artificial Intelligence (AI)) on June 13, 2025 at 1:56 am

  submitted by /u/ProfessionalKey5527 [link] [comments]

• “How an American musician is using AI to translate grief across cultures”
  by /u/Sonic_Improv (Artificial Intelligence (AI)) on June 13, 2025 at 1:52 am

  submitted by /u/Sonic_Improv [link] [comments]

• Meta Challenged Top Devs to Build an AI That Could Beat NetHack. No One Came Close.
  by /u/slhamlet (Artificial Intelligence (AI)) on June 13, 2025 at 1:13 am

  Unlike, say, a chess game, where each individual move is limited to a few dozen options, the moves in NetHack seem unlimited... It took me awhile to find these results online, and I sort of suspect Meta didn't do much to promote them, after no AI in the challenge managed to steal the Amulet of Yendor and ascend into heaven with it (NetHack's ridiculously near-impossible win condition). submitted by /u/slhamlet [link] [comments]

• How will AI vs real evidence be differentiated as AI gets more advanced?
  by /u/DoraTheRedditor (Artificial Intelligence (AI)) on June 13, 2025 at 1:04 am

  May not be the right place or a stupid question, sorry, I'm not too well versed in AI - but I do see photoshopped images etc. being used in major news cycles or the veracity of pictures being questioned in court proceedings. So as AI gets better, is there a way to better protect against misinformation? I'm not sure if there's a set way to identify identify AI and what isn't. ELI5 pls! submitted by /u/DoraTheRedditor [link] [comments]

• The movie RIPD (2013) was making characters with multiple fingers before it was cool.
  by /u/Emotional-Chipmunk12 (Artificial Intelligence (AI)) on June 13, 2025 at 12:41 am

  submitted by /u/Emotional-Chipmunk12 [link] [comments]

• How does this make you feel?
  by /u/AI-Admissions (Artificial Intelligence (AI)) on June 13, 2025 at 12:36 am

  I’m curious about other people’s reaction to this kind of advertising. How does this sit with you? submitted by /u/AI-Admissions [link] [comments]

• Building a non-exploitative AI tool for restaurant kitchens — looking for feedback from this community
  by /u/NoComputer6906 (Artificial Intelligence (AI)) on June 13, 2025 at 12:31 am

  I’m a former line cook who transitioned into tech, and I’m currently building a project called MEP (short for mise en place) with a scheduling frontend named Flo. The goal is to support restaurant teams—especially back-of-house crews—with shift coverage, prep coordination, and onboarding in a way that genuinely respects workers instead of surveilling them. This isn’t automation for automation’s sake. It’s not about cutting labor costs or optimizing people into exhaustion. It’s about designing a simple, AI-assisted system that helps small, chaotic teams stay organized—without adding more stress or complexity to already difficult jobs. Having worked in kitchens that used systems like HotSchedules and 7shifts, I’ve seen firsthand how these platforms prioritize management needs while making day-to-day work harder for the people actually on the line. MEP is meant to do the opposite. It helps assign roles based on real-world context like skill level, fatigue, and task flow—not just raw availability. It can offer onboarding prompts or prep walkthroughs for new cooks during service. Most importantly, it avoids invasive data collection, keeps all AI suggestions overrideable by humans, and pushes for explainability rather than black-box logic. I’m sharing this here because I want real feedback—not hype. I’m curious how folks in this community think about building AI for environments that are inherently messy, human, and full of unquantifiable nuance. What risks am I not seeing here? What are the ethical or technical red flags I should be more aware of? And do you think AI belongs in this kind of space at all? This isn’t a startup pitch. I’m not selling anything. I just want to build something my former coworkers would actually want to use—and I want to build it responsibly. Any insights are welcome, especially if you’ve worked on systems in similarly high-stakes, high-pressure fields. Thanks for your time. —JohnE submitted by /u/NoComputer6906 [link] [comments]