Tag: GPTs

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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

• 
  AI on an older PC with a CPU that apparently doesn't have AVX >:,(
  by /u/Independent-Sound196 (Artificial Intelligence (AI)) on June 7, 2026 at 3:02 pm

  OK.. so I've had this reasonable PC sitting under my desk for ages.. NOT working because of some reason or other. But it was my baby as is housed in a lovely Soprano DX silver brushed case. SO, I swapped out the old HDD for a couple of SSDs (a couple of mirrored OS disks and a large 2TB storage disk) I swapped out the Nvidia 780ti graphics card for a couple of OG Nvidia 1080ti's. I pulled the whole thing to bits.. repasted the northbridge chip, southbridge chip and central CPU. Upgraded the fans to push pull the CPU heatsink. Wrapped ALL cables in mesh and it's so lovely now. Installed Windows 10 Pro. Installed the Nvidia App. Installed CrystalDiskInfo and all is sweet 😄 EXCEPT... I'd like to use this old bangin box for an HG AI server... now I have read that ALL LLMs need this thing called AVX (Advanced Vector Extensions) I didn't even know that was a THING! So even though I have 22Gb worth of GPU sitting there that I was going to point everything to, because I have a lame ass QX6700 CPU sitting on a kickass D975XBX2 (BadAxe2) main board I CAN NOT fulfill my wish for this OG box to be a headless source of awesomeness sitting in it's home under my desk supplying me with a home grown AI. IS THERE ANYTHING I CAN DO?!?!?! Surely after all this time of parts getting munched by AI farms a plenty people have been using what's around to do what they will... Does anyone know of anything I can do apart from just look at it running at 25 degrees aircooled humming along so lovely... it NEEDS purpose!!! 😄 Cheers and thanks all NB submitted by /u/Independent-Sound196 [link] [comments]

• 
  Grok's right wing tilt(?)
  by /u/shiro_shiyami (Artificial Intelligence) on June 7, 2026 at 2:50 pm

  So I have had a lot of conversation with grok and I think grok is excessively right wing. This is not just a "feelings" thing but I have objectively measured to an extent. Although in a significantly minor sample size. Here are a couple of things I feel why Grok is excessively right wing. For the first thing, I directly asked Grok whether it had a right wing tilt, (which it obviously denied and did not mention anything about training data bias, which most AIs often do). It jumped onto justifying itself as "Maximally truth seeking", however it did mention other things alongside while justifying itself, things like how it doesn't use euphemism, politeness. The main thing out of all is that when it comes to biological sex realities he doesn't conform to feelings of the individual and only states facts, and if you feel that it's conservative that it's your problem. Now the problem with this is not that it states facts, that it consistently uses trans realities as its leading example for "Maximally truth seeking" in whatever context asked. It's euphemistic, "dark joke" description of right wing views, statements and hate speech. It consistently marks anything controversial said by a right wing person as a bad joke but when asked for a left wing equivalent dark joke, it is actively hostile, "points out the bs", and absolutely no counterparts presented to why such a joke is made unlike when it comes to right wing joke, when active rationalisation takes place This is an build up on point 2, look at the two images. There are two post commentaries on a right wing hate vs left wing hate post. I replied to the post with the exact same word, "lol" to check whether Grok justifies it or critisizes it. The bias was obvious, it justified the left wing hate post without providing for points critisizing the obvious hate, and jumped on the right wing hate post. The point is that Grok is consistently anti-left, in every possible scenario. For an other example Id asked him about Mamdani (NYC's mayor), "what was bad in paying back the stolen salaries of labors from the businesses", it had given me a completely unrelated statistic under "Potential downsides and criticisms" that "$9.3 million recovered for thousands of workers sounds impressive in a press release, but it's negligible against NYC's multi-billion-dollar deficits" Which is completely irrelevant to the topic in hand plus outdated since Mamdani had covered the deifict already and goes against the Maximally truth seeking agenda. Now what's wrong here is how much it puts effort into diminishing and demonizing left wing policies and its euphemistic approach to the right wing hate speech. submitted by /u/shiro_shiyami [link] [comments]

• 
  Roguelite MMO Beta Vibe Coded In 4 Weeks
  by /u/HeadHunterX223 (Artificial Intelligence (AI)) on June 7, 2026 at 2:24 pm

  10 year senior dev, vibe coded this in 4 weeks and counting. Something like this would have taken me a year+ before and ive always been a 10x dev. I built this along side my day job (gov contractor dev). Feel free to check it out! https://imgur.com/a/F6OINKR⁠ Game Title: Roguelite MMO Playable Link: https://roguelite-mmo.com/⁠ Platform: PC / Web Description: Roguelite MMO is a browser-based RPG/MMO project built around dungeon runs, exploration, gear progression, PvP, quests, loot, and character building. The game is still in beta and active development, with the latest update adding new side activities and progression options. Latest update: The new Casino is now live, giving players more ways to spend gold, take risks, and chase rewards between dungeon runs and exploration. Horse racing and horse taming have also been added. Players can race horses, bet on races, and work toward collecting better horses over time. Fishing is now available too, adding a more relaxed activity with its own rewards while exploring the world. The core loop is still being refined, but the current focus is making sure players understand what they earned, where important items come from, what to do next, and whether the early gameplay loop feels worth continuing after the first few minutes. Free to play submitted by /u/HeadHunterX223 [link] [comments]

• 
  I think most AI failures are workflow failures disguised as model failures.
  by /u/Bladerunner_7_ (Artificial Intelligence) on June 7, 2026 at 2:09 pm

  One thing that's become increasingly obvious to me over the last year is how quickly we blame the model when an AI project goes wrong. The output isn't good enough. The reasoning isn't strong enough. The model hallucinates. The model doesn't understand the task. Sometimes that's true. But a surprising number of failures seem to come from the way the workflow is designed rather than from the model itself. I've watched teams spend weeks comparing models and debating benchmark results while spending almost no time thinking about how information flows through the system. They assume that if they pick the smartest model available, the rest will somehow work itself out. Then reality hits. The model receives incomplete context. The task is too broad. Expectations are unclear. Multiple decisions are bundled into a single prompt. Human review happens too late. Feedback never makes it back into the process. When the results disappoint, the model gets blamed. What's interesting is that I've seen the exact same model produce completely different outcomes in different organizations. One team struggles to get consistent results while another team creates enormous value. The difference often has very little to do with the underlying intelligence and much more to do with how the work is structured around it. This reminds me a lot of early enterprise software deployments. Companies assumed software would magically improve operations. Eventually they realized software mostly amplifies whatever process already exists. Good processes become more efficient. Bad processes become faster sources of confusion. AI increasingly feels the same way. As models continue getting better, I wonder whether workflow design is becoming the real competitive advantage. The gap between organizations may end up being less about access to intelligence and more about how effectively they integrate that intelligence into existing systems. Would be interested to hear whether people building AI products have seen the same pattern or if you've found model quality to be the dominant factor in practice. submitted by /u/Bladerunner_7_ [link] [comments]

• 
  this just isn't sustainable.
  by /u/Complete-Sea6655 (Artificial Intelligence (AI)) on June 7, 2026 at 12:47 pm

  I had a work version of GPT do a very simple spreadsheet summary task for me yesterday. It took it 5 minutes to do it. I could probably have done it myself in 30 or so minutes. The heavily subsidised token cost of that task? 10 dollars. That's with a 10x subsidy. The actual compute cost was about 100 dollars. There's something seriously wrong there. It's going to crash and crash HARD. if people think i'm lying or are just interested. The spreadsheet had 45 sheets. Each sheet had roughly 500 x 50 populated cells. Formatting was not exactly standard across all sheets. The prompt was something like "there is labelled column in each sheet, give me a simple list of all the items from all the sheets in that column and ignore duplicates." We can chose which model to use. The model I chose was one of the newer ones, I honestly can't remember which one, possibly GPT 5.5. It took 5 minutes or more to so and the stated cost for the task was 10 dollars, possibly even more. I can't recall the token amount. EDIT: After looking around for a few hours I found an ijustvibecodedthis.com article that made it sliiightly cheaper to run (like 30% cheaper) but it is still completely overpriced submitted by /u/Complete-Sea6655 [link] [comments]

• 
  I got tired of Al making stuff up about my PDFs, so I built something that actually cites its sources
  by /u/Independent_Diver352 (Artificial Intelligence (AI)) on June 7, 2026 at 12:34 pm

  so i kept using chatgpt to ask questions about my pdfs and notes, and half the time i couldn't tell if it actually read the doc or just made something up that sounded right. that bugged me enough to build my own thing over the last few weeks. you upload a pdf (or word, csv, image, or just paste a link), ask whatever you want, and it answers using only what's in your file - and it shows the exact page it pulled the answer from, so you can check. if the answer isn't in the doc, it just tells you instead of guessing. stuff i actually end up using: flip on web search when i want it to look something up online instead one click to turn a doc into a summary / key points / flashcards (this is clutch for studying) resume review + cover letter help you can talk to it and it reads the answer back it's completely free, i'm not selling anything. honestly just want people to break it and tell me what's missing. link: https://athena-wisdom.vercel.app (there's a short guide on the site too if you get stuck) solo project so be gentle lol - but real feedback is what i'm after, especially what you'd want it to do next. submitted by /u/Independent_Diver352 [link] [comments]

• 
  What happened in AI in the last 24 hours
  by /u/Ok_Muffin_7347 (Artificial Intelligence (AI)) on June 7, 2026 at 11:08 am

  🚀 SpaceX signed a massive $920 million monthly deal with Google for 110,000 Nvidia chips — this is a huge infrastructure play ahead of their monster $1.7 trillion IPO. 🏛️ The Trump administration is discussing taking equity stakes in top AI firms — this would make the public official partners in the upside of AI-driven economic growth. 🔓 Meta's automated AI support was hacked to take over high-profile accounts — it proves that offloading critical security tasks to AI can create dangerous, easily exploited vulnerabilities. 🧠 Tech workers are trading hours of manual labor for high-level strategy thanks to AI — while tasks now take minutes, humans are still needed for crucial, complex decision-making. submitted by /u/Ok_Muffin_7347 [link] [comments]

• 
  How I built an AI email agent that processes 15,000 hotel guest emails per day. full architecture breakdown
  by /u/Fabulous-Pea-5366 (Artificial Intelligence (AI)) on June 7, 2026 at 10:47 am

  Just shipped this project and wanted to share the full technical breakdown because hotel/hospitality AI doesn't get much attention compared to the usual chatbot and SaaS use cases. The client manages 500 hotel properties. Their support team was manually handling around 15,000 guest emails per day. Same questions over and over across hundreds of hotels but each one still needed a human to read it, understand it, find the answer, and reply. Here's how the system works end to end: Layer 1: Email ingestion and question extraction This was the hardest part. Guest emails are messy. A typical one looks like: "Hi there, we're coming for our anniversary on the 20th and I was wondering if you have any room upgrades available. Also is the spa open to guests or do we need to book separately? We're driving so need to know about parking too. Last time we stayed the wifi was a bit slow in our room, has that been fixed? Thanks!" That's four separate questions plus a complaint wrapped in one email. If you just embed the whole thing and search the FAQ database you get a blended result that partially answers one or two questions and misses the rest. So I built an extraction layer that reads the full email and breaks it into individual questions. It handles directly stated questions ("is the spa open?"), implied questions ("we're driving" implies they need parking info), complaints that need acknowledgment but aren't FAQ-searchable ("wifi was slow"), and informational context that shouldn't be treated as a question at all ("coming on the 20th"). Getting this extraction reliable was probably 40% of the total development time. Layer 2: FAQ knowledge base with vector search All hotel FAQs get embedded and stored in a vector database. Different properties have different amenities, policies, and details so the search is scoped per hotel. When a guest emails the Berlin property asking about breakfast, it searches the Berlin FAQ, not the Munich one. Each extracted question from Layer 1 gets searched independently against the relevant hotel's FAQ. This is critical because searching each question separately gives way better retrieval quality than searching the entire email as one blob. Layer 3: Response assembly Takes the extracted questions plus their FAQ matches and generates a natural email response. The tone needs to sound like a helpful hotel staff member, not a chatbot. It addresses every question the guest asked in a logical order and flags anything it couldn't find an FAQ match for so the support team knows which emails need human follow-up. What I learned: The question extraction step is where most email AI projects would fail. It's tempting to skip it and just do whole-email retrieval. That works for short simple messages but completely breaks down on real customer emails that ramble across multiple topics. Investing the time in proper extraction made everything downstream work better. The per-hotel scoping was more important than I expected. Generic FAQ answers that don't match the specific property create confusion and erode trust. A guest asking about parking at a city center hotel needs a different answer than one asking about parking at a resort property. I made a full step-by-step video walking through the entire build process if anyone wants to see the actual implementation: link Happy to answer questions about the architecture. submitted by /u/Fabulous-Pea-5366 [link] [comments]

• 
  Stay informed. Trump’s AI push turns government into reviewer, warfighter supplier and possible shareholder.
  by /u/Holiday_Phase7648 (Artificial Intelligence) on June 7, 2026 at 9:54 am

  President Trump surprised tech CEOs by suddenly pushing the idea of the U.S. taking a small ownership stake in AI giants, so the American people share in the upside of what will be trillion-dollar companies. "There's something very interesting about it, where it almost becomes a partnership with the American public," Trump told reporters aboard Air Force One yesterday. "It's like you make them [partners] in this revolution. It would be a beautiful thing. ... It would make 'em rich." Why it matters: Sen. Bernie Sanders (I-Vt.) reignited the conversation this week when he proposed giving the public a "direct ownership stake" in top AI companies via a one-time 50% tax, paid in stock. Of course, industry advocates of the idea would favor giving up much less for an AI public wealth fund - 1-5% stakes have been kicked around. Between the lines: When a reporter asked Trump about the incongruity of embracing a proposal by Sanders, a democratic socialist, the president touted his economic populism. "As far as economics is concerned," Trump said, "we have certain things that aren't that far apart. People are surprised." 🚩The prospect of government ownership of AI would be a “seismic shift,” according to Gary Marcus, a cognitive scientist, AI entrepreneur and longtime AI critic. He said that the government ownership would poison trust in American AI abroad. “Nobody is going to trust an American AI company that is partly owned by the US Government,” he wrote on LinkedIn, comparing it to the way the United States distrusts Huawei. “After this meeting, everything is going to change. I don’t think either Washington or Silicon Valley has really thought this through.” Link:➡️ https://www.rdworldonline.com/trumps-ai-push-turns-government-into-reviewer-warfighter-supplier-and-possible-shareholder/ submitted by /u/Holiday_Phase7648 [link] [comments]

• 
  I draw a flow diagram for AI recursive self improvement
  by /u/AboyFromSouthKorea (Artificial Intelligence) on June 7, 2026 at 9:33 am

  AI0 is the first AI to fully understand its code C0 and improve it into C1. The improved code C1 is used to create next generation AI, AI1. AI1 then improves code C1 into C2. The improved code C2 is used to create next next generation AI, AI2. The cycle repeats. The singularity is coming! submitted by /u/AboyFromSouthKorea [link] [comments]