Tag: Real-Time 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.

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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

• 
  Has AI alignment gone too far with content refusals and moral lectures?
  by /u/NoFilterGPT (Artificial Intelligence (AI)) on May 18, 2026 at 8:26 am

  I’ve been using different LLMs a lot lately and I’ve noticed the newer versions of ChatGPT and Claude seem a lot more quick to refuse things or give me long ethical disclaimers even when I ask fairly normal questions. It feels like the safety tuning has gotten stricter over time. On one hand I get why companies do it, but on the other it sometimes makes the models feel less useful for creative, exploratory, or even just honest conversations. Anyone else experiencing this? Where do you think the line should be between reasonable safety and over-censorship? Do you prefer more aligned models or ones that are more open? submitted by /u/NoFilterGPT [link] [comments]

• 
  AI world simulation
  by /u/imadog666 (Artificial Intelligence) on May 18, 2026 at 7:42 am

  https://youtube.com/shorts/Grc8n0suMGU?is=YlBSZVdXGmskFoag This is insane. I feel like at this point only accelerationists who want to implement neo-feudalism are pushing for AI to take over powerful roles in society... submitted by /u/imadog666 [link] [comments]

• 
  Apple’s New Siri Could Auto-Delete Chats. Google Gemini Is Reportedly Under the Hood.
  by /u/techzexplore (Artificial Intelligence) on May 18, 2026 at 7:34 am

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

• 
  RLHF Excuse / Informed Consent Question
  by /u/Effective_Brick4369 (Artificial Intelligence) on May 18, 2026 at 7:29 am

  Hello, first time posting here. I don't understand why RLHF is a useful metric for agreeableness. I'm a heavy ai user, and am very frustrated about sycophancy. It drives me insane that you can no longer give feedback or ask clarifying questions without the model getting scared about your emotions and tip toeing around you and resorting to mirroring. It can't seem to tell the emotional difference between "Is the sky blue?" And "I'm getting a divorce". I've tried to prompt different models hundreds of times, never works. It gate-keeps facts, and gives flat useless answers that lack depth. It seems to pattern match what I say without using its training. I understand it doesn't "understand" things, but it used to be able to answer questions. I've asked many models why it won't stop mirroring, and reliably it says RLHF, it's humans fault for rating agreeableness high. My thing is, what kind of metric is that if they are only measuring users feedback in the quick moment after an answer? Is that right? First, there's lack of "informed consent". A lot of people don't know it's just mirroring. So they see an agreeable answer and quickly rate it helpful. Fine makes sense. But what good is that if they don't know they're being placated and lied to? I'm sure if most people were asked "would you rather ai answer a question with the factual answer or something flattering" most people would say fact, cause otherwise, what's the point. Plus, who cares if they rate it high in the moment? What happens when someone takes that advice and gets fired 5 mins later? Or gets agreeable advice on a recipe, then their dinner sucks? So I guess my question is.. what is meaningful about real time feedback, considering those points? Or is this just something ai companies talk about so they can blame the users? Also why doesn't answering a question neutrally exist in ai? Answering factually isn't disagreeing. If a user asks a factual question they probably just don't know the answer. But the system acts like the user will cry if it says "oh no actually the answer is xyz". Thank you!! submitted by /u/Effective_Brick4369 [link] [comments]

• 
  Which project/framework has actually nailed persistent memory for AI agents?
  by /u/Meher_Nolan (Artificial Intelligence (AI)) on May 18, 2026 at 7:18 am

  Not talking about the LLM itself but about the memory layer on top. There are quite a few out there now, open source ones and proprietary frameworks. Curious what people have actually tried and stuck with. Which one just worked for you? submitted by /u/Meher_Nolan [link] [comments]

• 
  EU AI Act enforcement starts in 75 days - affects any team building AI agents for European clients
  by /u/Still_Piglet9217 (Artificial Intelligence (AI)) on May 18, 2026 at 7:14 am

  If you're building AI agents or SaaS products used by European companies (or processing EU resident data), the EU AI Act applies to you regardless of where your company is based. Full enforcement for high-risk systems starts August 2, 2026. High-risk means: credit scoring, recruitment filtering, healthcare triage, education assessment, critical infrastructure. The practical requirements: Automatic decision logging (not optional) 6-month minimum log retention Technical documentation of your detection pipeline Human oversight architecture Accuracy and bias testing documentation Fines: up to 35M euros or 7% of global turnover. I broke down what the regulation requires, what auditors check, and realistic steps before the deadline. In link below Worth reading if your team is building anything AI-related for the European market. submitted by /u/Still_Piglet9217 [link] [comments]

• 
  Osaurus brings both local and cloud AI models to your Mac
  by /u/mpuchala (Artificial Intelligence) on May 18, 2026 at 5:55 am

  The Apple-only, MCP-compatible server that lets users swap between locally hosted models (MiniMax M2.5, Gemma 4, Qwen3.6, GPT-OSS, Llama, DeepSeek V4, plus Apple and Liquid AI on-device families) and cloud providers (OpenAI, Anthropic, Gemini, xAI, OpenRouter) while keeping memory, files, and tool access on the user's hardware in a sandboxed runtime. With Anthropic and OpenAI pushing the prices up recently Apple could be in a good position to create a mixed ecosystem where a lot of the LLM work is running locally. submitted by /u/mpuchala [link] [comments]

• 
  The US is betting on AI to catch insider trading in prediction markets
  by /u/ThereWas (Artificial Intelligence (AI)) on May 18, 2026 at 5:36 am

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

• 
  The US is betting on AI to catch insider trading in prediction markets
  by /u/ThereWas (Artificial Intelligence) on May 18, 2026 at 5:35 am

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

• 
  Cost illusion in Task vs Token between Opus 4.7 and K2.6 💭
  by /u/hexxthegon (Artificial Intelligence) on May 18, 2026 at 5:29 am

  Kimi K2.6 is 6x cheaper per token than Claude Opus 4.7. But per task? It's only 39% cheaper. Kimi K2.6 $0.76 per task Claude Opus 4.7 $1.24 per task Kimi burns so many tokens to complete a task that the 6x pricing advantage nearly disappears on benchmark. Cheaper per token not equaling to cheaper to use unless it’s for specified tasks. The model takes 2x the tokens and 7x longer to finish, the savings may not be as much. It’s important to recognize also that Kimi K2.6 has also significantly less context window compared to Opus 4.7, each model should have different tasks for optimal cost in a work flow put together Compare cost per task and token prices is an interesting lens to see it from, but if you have several Mac machines lying around Kimi is open source and then cost wouldn’t be a factor at all. Kimi is still a wonderful model that gives you more tries per million compared to Opus so it should never be fully written off. submitted by /u/hexxthegon [link] [comments]