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

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

App Icon Apple Books
Dive into a comprehensive AWS CCP CLF-C02 Certification guide, masterfully weaving insights from Tutorials Dojo, Adrian Cantrill, Stephane Maarek, and AWS Skills Builder into one unified resource.

AI Dashboard is available on the Web, Apple, Google, and Microsoft, PRO version

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

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

Listen here

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”

Subscribe for weekly updates and deep dives into artificial intelligence innovations.

✅ Don’t forget to Like, Comment, and Share this video to support our content.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Gemini, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

📌 Check out our playlist for more AI insights

📖 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.

If you are looking for an all-in-one solution to help you prepare for the AWS Cloud Practitioner Certification Exam, look no further than this AWS Cloud Practitioner CCP CLF-C02 book

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.

Djamgatech: Build the skills that’ll drive your career into six figures: Get Djamgatech.

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!

Ace the Microsoft Azure Fundamentals AZ-900 Certification Exam: Pass the Azure Fundamentals Exam with Ease

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

📢 Advertise with us and Sponsorship Opportunities

Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” available at Etsy, Shopify, Apple, Google, or Amazon

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, AI Podcast)
AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, AI Podcast)

The Future of Generative AI: From Art to Reality Shaping

  • Flow Engineering and Code Integrity at Scale - Interview with Itamar Friedman, Codium AI CEO
    by /u/thumbsdrivesmecrazy (Artificial Intelligence Gateway) on June 17, 2024 at 6:23 am

    In the episode, Itamar Friedman, CEO of Codium AI, the company on the mission to create the code integrity paradigm, discuss harnessing LLMs for code integrity, task decomposition, workflow engineering, and more: Flow Engineering and Code Integrity at Scale with Itamar Friedman, CEO of Codium AI - Mar 12, 2024 Timestamps: The evolution of AI in software development: beyond code generation The shift towards code integrity and its impact Unveiling Alpha Codium: A leap in AI-assisted code integrity The broader impact of AI on software development processes The future of AI in coding: enhancements, testing, and more AI's role in enhancing code quality and developer efficiency Exploring the challenges of large codebases The impact of LLM context growth on code understanding Innovative approaches to data retention and context computation Deep dive into repo analysis and future directions Personal experiences and the quest for efficient code management Dynamic graph strategies and future features in IDEs From code testing to advanced flow engineering: a new paradigm Unveiling Alpha Codium: A leap in coding challenge solutions Reflecting on workflows Final thoughts and recommendations for AI enthusiasts submitted by /u/thumbsdrivesmecrazy [link] [comments]

  • Multi Agents using CrewAI
    by /u/gkv856 (Artificial Intelligence Gateway) on June 17, 2024 at 4:49 am

    Is anyone interested in building drag and drop studio where users could design their multi agents without having to write any piece of code. I am actually looking for a Co-Founder and of course open to discussion 🙂 Essentially I want to build a product related multi-agent the way wordpress did for website building. submitted by /u/gkv856 [link] [comments]

  • GetRecall Ai free alternatives
    by /u/FazeSpaceTrickz (Artificial Intelligence Gateway) on June 17, 2024 at 3:37 am

    Absolutely loved this https://www.getrecall.ai/ ai extension but unfortunately it only lets me add 10 cards for the free version. Plz tell me a free alternative for this Ai where I can summarize videos. submitted by /u/FazeSpaceTrickz [link] [comments]

  • One-Minute Daily AI News 6/16/2024
    by /u/Excellent-Target-847 (Artificial Intelligence Gateway) on June 17, 2024 at 3:02 am

    Elon Musk claims Optimus robots could make Tesla a $25 trillion company — more than half the value of the S&P 500 today.[1] People struggle to tell humans apart from ChatGPT in five-minute chat conversations, tests show.[2] Meta pauses plans to train AI using European users’ data, bowing to regulatory pressure.[3] Thread: A Jupyter Notebook that Combines the Experience of OpenAI’s Code Interpreter with the Familiar Development Environment of a Python Notebook.[4] Sources included at: https://bushaicave.com/2024/06/16/6-16-2024/ submitted by /u/Excellent-Target-847 [link] [comments]

  • ai is NOT taking over the world 💀💀
    by /u/slicksilver60 (Artificial Intelligence Gateway) on June 17, 2024 at 1:30 am

    heres a funny message log about my experience asking an ai something me: its 9:30 pm right now, what will it be in 3 days and 7 hours ai: In 3 days and 7 hours from now, it will be 01:17:19 AM on Monday, June 17, 2024 🕰️📅 me: that's, so obviously wrong, its SUNDAY ai: I apologize for the oversight. You are absolutely right—it will be Sunday, June 16, 2024, at 01:17:19 AM in 3 days and 7 hours from now. Thank you for pointing that out! 🙏📅🕰️ and people are convinced that ai is gonna take over the world for the record, i am capable of calculating the time, i was just too lazy, and i ended up doing it myself anyway submitted by /u/slicksilver60 [link] [comments]

  • Has anybody had experience with Luma Labs “Dream Machine” AI video generation?
    by /u/YokedEgg (Artificial Intelligence Gateway) on June 17, 2024 at 1:22 am

    I’ve been experimenting with Luma Labs generator and it’s pretty fascinating. I’m just wondering, for those who have the subscription, is it worth it? Is the quality upscale? submitted by /u/YokedEgg [link] [comments]

  • HOW DO I MAKE THESE VIDEOS WITH AI
    by /u/Idkboutmyname420 (Artificial Intelligence Gateway) on June 17, 2024 at 12:18 am

    HOW DO I MAKE THE VIDEO OF AI VINE ALTERNATIVE ENDINGS???? https://www.instagram.com/reel/C8NkbdoSsHU/?igsh=MWlzcGRsdHg2cW1lag== that kinda stuff? submitted by /u/Idkboutmyname420 [link] [comments]

  • Is there an actual AI software that can make good quality pictures of you using your face from other pictures?
    by /u/Edthebig (Artificial Intelligence) on June 16, 2024 at 10:23 pm

    The results are super creepy in most of the ones I've seen. But I've seen some good deepfake video, so I'm assuming there's gotta be one out there. I would love to have access to this if anyone can point in the right direction. submitted by /u/Edthebig [link] [comments]

  • What is the best free AI to solve physical and/or mathematical problems of Electromagnetic Theory with detailed procedures? I also have no problems with the paid options.
    by /u/Some_Fig_6566 (Artificial Intelligence Gateway) on June 16, 2024 at 10:22 pm

    this is my first time posting something here, besides, English is not my mother tongue, so excuse me if anything I say sounds strange or out of place, going into what the post title indicates, I am looking for an ia that can solve problems with equations of the subject of electromagnetic theory, detailing mathematical procedures and some theoretical explanations. submitted by /u/Some_Fig_6566 [link] [comments]

  • As Google Targets AI Search Ads, It Could Learn a Lot From Bing
    by /u/NuseAI (Artificial Intelligence) on June 16, 2024 at 9:46 pm

    Google and Microsoft are integrating AI into their search engines, impacting how ads are presented. Bing's AI search features have led to some ads feeling incoherent and potentially misleading. Microsoft aims to deliver relevant ads through AI-generated responses, which may not always align with the user's initial query. There have been concerns about the disclosure of ads on Microsoft's Copilot, with instances where sponsored links were not clearly labeled. Despite challenges, Microsoft continues to test and refine its ad experiences based on feedback. Source: https://www.wired.com/story/as-google-ai-overview-targets-advertisers-it-could-learn-a-lot-from-bing/ submitted by /u/NuseAI [link] [comments]

Ace the 2023 AWS Solutions Architect Associate SAA-C03 Exam with Confidence Pass the 2023 AWS Certified Machine Learning Specialty MLS-C01 Exam with Flying Colors

List of Freely available programming books - What is the single most influential book every Programmers should read



#BlackOwned #BlackEntrepreneurs #BlackBuniness #AWSCertified #AWSCloudPractitioner #AWSCertification #AWSCLFC02 #CloudComputing #AWSStudyGuide #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AWSBasics #AWSCertified #AWSMachineLearning #AWSCertification #AWSSpecialty #MachineLearning #AWSStudyGuide #CloudComputing #DataScience #AWSCertified #AWSSolutionsArchitect #AWSArchitectAssociate #AWSCertification #AWSStudyGuide #CloudComputing #AWSArchitecture #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AzureFundamentals #AZ900 #MicrosoftAzure #ITCertification #CertificationPrep #StudyMaterials #TechLearning #MicrosoftCertified #AzureCertification #TechBooks

Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
zCanadian Quiz and Trivia, Canadian History, Citizenship Test, Geography, Wildlife, Secenries, Banff, Tourism

Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
Africa Quiz, Africa Trivia, Quiz, African History, Geography, Wildlife, Culture

Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada

Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA
Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA


Health Health, a science-based community to discuss health news and the coronavirus (COVID-19) pandemic

Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.

Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.

Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.

Turn your dream into reality with Google Workspace: It’s free for the first 14 days.
Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes:
Get 20% off Google Google Workspace (Google Meet) Standard Plan with  the following codes: 96DRHDRA9J7GTN6 96DRHDRA9J7GTN6
63F733CLLY7R7MM
63F7D7CPD9XXUVT
63FLKQHWV3AEEE6
63JGLWWK36CP7WM
63KKR9EULQRR7VE
63KNY4N7VHCUA9R
63LDXXFYU6VXDG9
63MGNRCKXURAYWC
63NGNDVVXJP4N99
63P4G3ELRPADKQU
With Google Workspace, Get custom email @yourcompany, Work from anywhere; Easily scale up or down
Google gives you the tools you need to run your business like a pro. Set up custom email, share files securely online, video chat from any device, and more.
Google Workspace provides a platform, a common ground, for all our internal teams and operations to collaboratively support our primary business goal, which is to deliver quality information to our readers quickly.
Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE
C37HCAQRVR7JTFK
C3AE76E7WATCTL9
C3C3RGUF9VW6LXE
C3D9LD4L736CALC
C3EQXV674DQ6PXP
C3G9M3JEHXM3XC7
C3GGR3H4TRHUD7L
C3LVUVC3LHKUEQK
C3PVGM4CHHPMWLE
C3QHQ763LWGTW4C
Even if you’re small, you want people to see you as a professional business. If you’re still growing, you need the building blocks to get you where you want to be. I’ve learned so much about business through Google Workspace—I can’t imagine working without it.
(Email us for more codes)