Tag: AI Fine-Tuning

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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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📖 Read along with the podcast below:

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

• 
  This is the one simple thing I don't like about Ai. And it's so glaring and such a big problem few people are paying attention to really fixing.
  by /u/Zonties (Artificial Intelligence) on February 19, 2026 at 7:41 am

  I think everyone is missing one super important thing :logic. It costs one million tokens per querery and does it's calculations secretly and separately from getting contaminated with other data. Tech great. Looking up facts great. Language Great Reasoning non existent Calculations in private to solve these problems and help it with its memory and avoid contamination. But then how does it know the user isn't contaminating the data Incredibly sentient It doesn't know the difference(the actual distance) between up and down. Colors. Just poses and hexadecimals that match colors. It csn not understand why one image is over yhr earth and one over the sea. Like from a computer game. It simply tried to match the most accurate pixels. In my example it's very understandable why it chose wjst it did. My query was about a 'big bad boss with a million hit points.' it tried to match wjst I said by finding the YouTube videos with the closest. Matches of hit points and the pixel matches did look different so a two year old child could get it in a second. But if you took it's answer as truth, that could lead to serious consequences in any business setting. This is the problem. This is why it's artificial. This is why it concerns me greatly but as I've learned it'd definitely not for skynet. Queries agree with you. You have a different opinion and it always makes it passively, even directly align with your answer. It can tell pixels on faces and emotional expression but only because it's on the pixels. This makes it's real intelligence as we think of it zero. It simply regurgitates the next word it sees fit in ifd form, and the more you use it the more you will understand the business side of it if you really think... Unless it's fixed and not feign fixed like the older hands problem, I think it's inevitable this becomes a huge, huge financial problem. I'm not trying to do chart analysis here. Maybe it's since I'm neurodivergent, but there are many risks with ai now. But bar non this appears to be the greatest and historically catastrophic possible cascade of outcomes (that it could lead to. In real world, messy scenarios where rules are subjective. A hand out may be confusing for it, as it could mix it up if thr other hand was like "come" for a dog) the dog could understand that but Ai couldn't. And since it can't understand the why in the context, like a grossing road it could lead to some sort of really life cascade of problems if it kept relying on thr last answer we true, even though certain new technologies are reducing this, Nokia ) glass box initiative by filtering out thr bad dats, and veras! Nvidia?! Meta. submitted by /u/Zonties [link] [comments]

• 
  How can I automate the filling of an online form?
  by /u/javiercrespoai (Artificial Intelligence) on February 19, 2026 at 7:37 am

  Hello community, I want to automate the filling of forms using automation and AI, with less expenses possible for my work. What I want to do is scanning documents to extract some data, and fill the form with these data all in one process. I would only need to supervise and check that all data is correct before sending the form. Any ideas of how can I do it? submitted by /u/javiercrespoai [link] [comments]

• 
  Is OpenAI Following the Scaling Law to Bankruptcy? OpenClaw vs. The Behemoth
  by /u/Severe_Lion938 (Artificial Intelligence) on February 19, 2026 at 7:31 am

  OpenAI is currently the world's most expensive science project. The "Scaling Law" is hitting a wall of diminishing returns, and the burn rate is astronomical. We've been doing some deep analysis on r/myclaw about the viability of these closed-source giants. How many billions can you lose before "AGI" is just a fancy way of saying "bankrupt"? While they struggle with legacy bloat and massive compute costs, lean, open alternatives like OpenClaw are proving that you don't need a small country's power grid to be useful. OpenAI might be the "biggest failure" in history because they bet everything on the idea that more data = more intelligence, ignoring the fact that the internet is now 50% AI-generated garbage. The bubble is leaking, and it's going to be hilarious when the "gods of AI" have to ask for a government bailout. submitted by /u/Severe_Lion938 [link] [comments]

• 
  FIG Stock: No AI Software Disruption; Too Soon to Conclude?
  by /u/ugos1 (Artificial Intelligence) on February 19, 2026 at 7:16 am

  submitted by /u/ugos1 [link] [comments]

• 
  How to allow agents interact with on device applications?
  by /u/adityashukla8 (Artificial Intelligence) on February 19, 2026 at 6:58 am

  I'm figuring out approach for a multi-agent voice first real-time workflow where agent(s) can interact with on device applications like WhatsApp, Spotify, alarm, calender etc. an agent that becomes the user's hands on screen. The agent observes the browser or device display, interprets visual elements with or without relying on APIs or DOM access, and performs actions based on user intent. The agents will be developed with Google ADK and it'll be hosted as a webapp. Example: "check what are the unread messages on WhatsApp/any app" "Set a reminder at 5 pm" "Remind me to take medicine everyday at 12 pm" submitted by /u/adityashukla8 [link] [comments]

• 
  Built an agent that applied to 1,000 jobs in 48 hours
  by /u/Thick_Professional14 (Artificial Intelligence (AI)) on February 19, 2026 at 6:56 am

  https://reddit.com/link/1r8sbl0/video/lwjy5ybzfekg1/player The agent gets two things: a snapshot of the browser and a tree showing every element it can click or fill. That's how it knows what's on the page and what it can interact with. From there it reasons through the form on its own. No hardcoded field mapping, no brittle selectors. It just looks at what's there and figures it out. What surprised me was how it handled situations I didn't plan for. LinkedIn session expired mid-application it reset the password and kept going. One listing had no form at all, just a contact email it sent the email directly with my resume. One application was in French it completed the whole thing in French. I didn't build any of that in. It just reasoned through it. 1,000 applications, 2 days, multiple interviews lined up. Open source: https://github.com/Pickle-Pixel/ApplyPilot submitted by /u/Thick_Professional14 [link] [comments]

• 
  It's only with me or your GPT 5.2 is completely crazy about one week till now?
  by /u/DareToCMe (Artificial Intelligence (AI)) on February 19, 2026 at 6:49 am

  I know that is a rollout coming and the backend of openAi is I red code... But recently it's simply impossible to work with anything in GPT that needs any simple task... If you send an OCR... It is read wrong, then you get angry, helps to fix it and ask a simple txt with content for instance and GPT does... So you ask this simple task... Generate the file for download in .txt or .md and then the issues back again missing content... Resuming... I'm going crazy because GPT for one week already. Anybody with same simple issues like that? Cheers submitted by /u/DareToCMe [link] [comments]

• 
  Are structured AI agent workshops worth it, or is self-learning enough?
  by /u/FoundSomeLogic (Artificial Intelligence) on February 19, 2026 at 6:46 am

  I am continuously learning about AI agents mainly through documents available, videos, or Git repos but I feel like I am missing the architecture side of things. You can build small demos with tool calling, but when it comes to memory handling, multi-agent coordination, failure states, observability/debugging, making agents reliable it starts getting messy pretty fast. I came across a small 2-day weekend cohort focused specifically on building AI agents by Valentina Alto, Lior Gazit, and Leonid Kulign but I am confused whether programs like that are actually worth it compared to just figuring things out on own. Honestly, I have read few of their books which made sense to me and were easy to understand and practical. I needed opinion if such cohorts are really helpful Or is hands-on experimentation enough? I am feeling to attend it this time but really confused around it. submitted by /u/FoundSomeLogic [link] [comments]

• 
  Trying to understand how AI actually works behind the scenes — where do I start?
  by /u/BlushyBlaze (Artificial Intelligence) on February 19, 2026 at 6:21 am

  I’ve been seeing AI everywhere lately and I feel like I’m late to the party. The problem is I don’t come from a hardcore tech background, so most explanations online either feel too simplified or extremely technical. What I’m really struggling with is understanding what’s actually happening in the background when people talk about AI. Like when someone says a model is trained, what does that really mean in practical terms? Is it just a lot of data being fed into a system until it starts recognizing patterns, or is it something more complicated than that? And when you use something like ChatGPT or any AI tool, what is actually happening between typing a prompt and getting a response back? I’m not trying to become an engineer right now, I just want to understand the basics well enough so it stops feeling like some black box magic. At the moment it feels like everyone else understands this except me, which is probably not true, but still. If you’ve gone from zero to having a decent understanding of AI, what helped things finally click for you? submitted by /u/BlushyBlaze [link] [comments]

• 
  Actual AI usage data can be very different from what people assume
  by /u/Legitimate_Worker_21 (Artificial Intelligence) on February 19, 2026 at 5:27 am

  There’s constant debate about ChatGPT vs Claude vs Gemini, but most people don’t really see their actual usage over time. Recently saw some metrics from aimetrical, and the difference was bigger than expected. One tool had hundreds of prompts, while another that was still on a paid plan was barely used. What stood out more was the sensitive content detection. It flagged things like emails and credentials before sending, which made it clear how easy it is to paste something without thinking. It made me wonder how many people are paying for tools they don’t really use, or sharing more than they realize. Has anyone else looked at their actual usage data? submitted by /u/Legitimate_Worker_21 [link] [comments]