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

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

🤖🚀 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.

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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.


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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.

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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

  • One-Minute Daily AI News 1/20/2025
    by /u/Excellent-Target-847 (Artificial Intelligence (AI)) on January 21, 2025 at 5:46 am

    Trump revokes Biden executive order on addressing AI risks.[1] OpenAI’s ChatGPT crawler can be tricked into DDoSing sites, answering your queries.[2] Chemical research often contains inaccurate mass measurement data, according to AI analysis.[3] AI-designed proteins tackle century-old problem — making snake antivenoms.[4] Sources: [1] https://www.reuters.com/technology/artificial-intelligence/trump-revokes-biden-executive-order-addressing-ai-risks-2025-01-21/ [2] https://www.theregister.com/2025/01/19/openais_chatgpt_crawler_vulnerability/ [3] https://phys.org/news/2025-01-chemical-inaccurate-mass-ai-analysis.html [4] https://www.nature.com/articles/d41586-025-00133-z submitted by /u/Excellent-Target-847 [link] [comments]

  • The day
    by /u/MiniJamesWorthy (Artificial Intelligence Gateway) on January 21, 2025 at 5:34 am

    January 20, 2025...the day Artificial Intelligence realized that human beings were a waste of oxygen. submitted by /u/MiniJamesWorthy [link] [comments]

  • AI in 2025: Beyond Saas, It's Becoming a Partner
    by /u/numbcode (Artificial Intelligence Gateway) on January 21, 2025 at 4:53 am

    We’ve always talked about AI as a tool, but as we step into 2025, it's becoming something much more – a partner. AI agents now don’t just answer questions; they manage projects, brainstorm ideas, and even make creative decisions. For instance, imagine launching a startup where your "co-founder" is an AI that helps with market research, pitches, and even coding! On the flip side, this shift raises a fascinating challenge: how do we draw the line between human creativity and AI assistance? Are we ready to share credit with machines for things we create together? What are your thoughts? Would you trust an AI as a partner in your business, art, or daily life? Or does this feel like giving too much control away? submitted by /u/numbcode [link] [comments]

  • Advanced Uses of AI ?
    by /u/metasubcon (Artificial Intelligence Gateway) on January 21, 2025 at 3:48 am

    I'm a layman and my uses of ai are pretty simple. I use chatgpt for learning, other generative tools for image and other content generation and I only know of such use. Yes I can understand these can be used for various content and market related needs. But apart from that I also noticed that there are various advanced uses and I know some terms but couldn't find what are the uses, even though I have read things related to it so : 1) what's api and what's it's use in ai ! 2) what is the meaning in saying train ur own model or customised models ! 3) how ai apps can be made using existing stuff ? 4) any other such advanced uses ? 5) I have searched, but is there a place where they teach all these, somewhere u liked submitted by /u/metasubcon [link] [comments]

  • What if Foundry VTT had its own AI Dungeon Master?
    by /u/Dinosaurrxd (Artificial Intelligence (AI)) on January 21, 2025 at 3:40 am

    I’ve been developing Foundry Architect, an AI-powered assistant for Foundry VTT that’s designed to handle combat, narrate actions, and make tactical decisions—all in real time. It works seamlessly with Foundry’s existing automation and APIs, blending cutting-edge AI with your favorite tools to create something that feels dangerously close to an actual AI DM. Imagine goblins flanking tactically, wizards casting spells at the perfect moment, and immersive narrations appearing as the story unfolds—all without your input. Foundry Architect doesn’t just assist; it thinks. It’s not perfect, but it’s getting close. Curious to hear: Would you use something like this in your games? What excites or scares you about an AI DM? Let’s talk about where this could go! submitted by /u/Dinosaurrxd [link] [comments]

  • Can we implement Recursive Self-Improvement on current models?
    by /u/thinkNore (Artificial Intelligence Gateway) on January 21, 2025 at 2:31 am

    Do we know how to implement Recursive Self-Improvement in current models? How easy/difficult is it? Is it more like an on/off implementation or an incremental development/deployment? submitted by /u/thinkNore [link] [comments]

  • Consumer comfort using AI has gone down a massive 11 % pts year-over-year, and only 1 in 4 consumers trust organizations to use it responsibly.
    by /u/MaxGoodwinning (Artificial Intelligence Gateway) on January 21, 2025 at 2:22 am

    Based on this survey of nearly 24,000 people across 23 countries. Are you surprised that it's gone down since it became "functional"? I feel like it makes sense considering the initial hype has worn off and now people are facing the reality of its current limitations, although I feel like people will (rightfully) continue to be skeptical of its use by organizations. submitted by /u/MaxGoodwinning [link] [comments]

  • for those who minimize the milestone just achieved by uc berkeley's sky-t1
    by /u/Georgeo57 (Artificial Intelligence Gateway) on January 21, 2025 at 2:14 am

    "they JUST fine-tuned an existing model." "they JUST fine-tuned it for a very specific task." "they JUST showed how overfitting of llm's is done." all of that completely misses the point. it doesn't matter how they do it. what matters is what it does, especially when compared with much larger proprietary models costing much more to build and train. they did it with less than $450 they did it in 19 hours they did it with only 8 h100s it outperforms o1 preview on math500 it outperforms o1 preview on aime and livebench it's the first truly open source reasoning model in the sense that it can be replicated from scratch it runs on consumer hardware with a gpu (24GB+) it's only 32 billion parameters again it doesn't matter how it's done, what matters is what it does, and how much it takes to do it. deepseek just amazed the world with their open source v3 and r1 models. the rate of progress in ais, and especially in open source models, is accelerating rapidly. don't be surprised if within the next 3 months an open source model is released that matches o3. and if they do it by JUST fine-tuning, don't let that distract you from what they've done. submitted by /u/Georgeo57 [link] [comments]

  • Towards safe ASI - would this help?
    by /u/No_Lime_5130 (Artificial Intelligence (AI)) on January 21, 2025 at 12:55 am

    Could LLMs be inheriting a deep-rooted “war-power” bias from humanity’s evolutionary past? Early human communities competed fiercely for resources, and these narratives of conflict and dominance are woven into much of our text and media. If AI is trained on all that content—complete with war metaphors and power-centric stories—does it risk developing an overemphasis on conflict-driven “solutions”? One idea would be to filter or reduce the most aggressive content to avoid an overrepresentation of old survival strategies. Another approach would be supplementing AI training with insights or texts from cooperative species (real, like bonobos, or purely hypothetical societies using high quality synthetic data) to emphasize empathy and collaborative problem-solving. A naive test would be if the loss of an LLM is high or low given some extremely low empathy content. If the LLM cannot predict what "skynet" does next, i would feel saver. submitted by /u/No_Lime_5130 [link] [comments]

  • A couple questions for the folks in the know
    by /u/ShadNuke (Artificial Intelligence Gateway) on January 21, 2025 at 12:15 am

    Recently, my 5 year old grandson and I were watching YouTube shorts, and we saw a clip of Andrew Santino and Bobby Lee on their Best Friends podcast. The clip I'm referring to is of the guys making fun of each others mother. When he saw it, he laughed his little ass off, and he is still laughing about it today!! We saw the video in question over a week ago! Now, he's walking around imitating Andrew, but has replaced what he says with his own name... Oli. So now he's been referring to my daughter as "Oli Mom!", while imitating Andrew Santino. I was wondering if it would be possible to create an AI modified version of a clip of that YouTube short to have Andrew use the name Oli, instead of Bobby? I wouldn't know where to start, as I'm an old head, and AI usage is well above no skills🤣. Could someone point me in the right direction, to see if I get this made for him and my daughter, or point me to some software that I could potentially do it myself? Any help would be greatly appreciated! Here's a link to the NSFW Bad Friends Podcast clip in question Thanks in advance! submitted by /u/ShadNuke [link] [comments]



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