0.000000 2.560000 Tech It From Me is an independent and solo-produced podcast. 2.560000 4.400000 Welcome to the Tech It From Me podcast. 4.400000 5.760000 I'm Mike Madole. 5.760000 9.360000 Everyone's talking about large language models these days. 9.360000 14.960000 GPT-4, Cloud, Gemini, but very few people can actually explain 14.960000 17.600000 what these things are or how they work. 17.600000 20.400000 Are they just giant, bottle-complete machines? 20.400000 22.400000 Or are they something more? 22.400000 23.600000 Do they think? 23.600000 25.200000 Do they search? 25.200000 28.240000 Today, we're going behind the buzzwords to break it all down. 28.240000 31.280000 I'll walk you through how large language models work 31.280000 35.840000 from tokens and transformers to embeddings and predictions 35.840000 38.400000 in a way that makes sense whether you're an engineer, 38.400000 41.760000 an executive, or just curious about the future. 41.760000 44.320000 This is the Tech It From Me podcast. 44.320000 45.680000 Let's go. 45.680000 48.240000 This is the Tech It From Me podcast. 48.240000 51.600000 You don't need to know how to code to understand what a large 51.600000 55.600000 language model is. You just need the right analogy. 55.600000 58.160000 Think about Cloud computing a decade ago. 58.160000 62.400000 Everyone was throwing the term around, but few really understood it. 62.400000 65.840000 I didn't stop businesses from investing millions into it. 65.840000 70.160000 And today, we're seeing the same thing happen with AI, 70.160000 74.960000 especially with large language models or LLMs. 74.960000 79.600000 If you're in a leadership role in IT, HR, operations, 79.600000 83.040000 legal, marketing, you might not be writing code, 83.040000 88.480000 but you're definitely going to be making decisions that involve AI tools. 88.480000 92.960000 Maybe it's approving budgets, maybe it's reviewing vendor proposals, 92.960000 96.000000 perhaps it's fielding questions from your board 96.000000 98.720000 about your company's AI strategy. 98.720000 103.200000 And the truth is, you can't afford to just not along anymore. 103.200000 106.000000 Because chances are, someone in your organization 106.000000 109.200000 is already pushing to bring in AI. 109.200000 111.600000 Maybe it's a chatbot project. 111.600000 115.520000 Maybe it's document summarization with co-pilot. 115.520000 119.280000 Maybe someone is using chat GPT in their daily workflow 119.280000 125.200000 without you even knowing. And when they say we could use an LLM for that, 125.200000 128.480000 most people nod politely and think that sounds great, 128.480000 132.080000 but I have no idea what that means. 132.080000 137.280000 That's not a great place to be, especially when the tech is moving this fast. 137.280000 140.800000 Now, before we go deep into the mechanics, I want to quickly share my 140.800000 143.680000 background with artificial intelligence. 143.680000 149.280000 I hold certifications in career essentials and generative AI by Microsoft, 149.280000 155.600000 ethics in the age of generative AI, and generative AI for digital marketers. 155.600000 159.520000 I also am working on a hobby project that embraces AI solutions, 159.520000 163.120000 such as chatbots and AI document management, 163.120000 166.240000 and a cost-effective price point for consumers. 166.240000 169.360000 So this isn't just a passing interest. 169.360000 175.280000 It's something I've been exploring from both a technical and a strategic lens. 175.280000 178.880000 So what is a large language model? 178.880000 184.800000 At its core, a large language model or LLM is a kind of artificial intelligence 184.800000 189.680000 trained to understand and generate human-like text. 189.680000 192.800000 But it's not reading or thinking in the way we do. 192.800000 197.200000 It's using probability to make predictions. 197.200000 200.480000 And when we say large, we mean massive. 200.480000 205.600000 These models are trained on data sets that span the entire internet, 205.600000 211.840000 books, news articles, research papers, discussion forums, Wikipedia, 211.840000 217.120000 product manuals, marketing emails, social media posts, and more. 217.120000 223.120000 Some estimates say the most advanced models have been trained on over a trillion words, 223.120000 228.400000 which is the rough equivalent of 12.5 million novels. 228.400000 230.320000 But here's what's important. 230.320000 233.600000 LLMs do not memorize facts. 233.600000 236.080000 They actually learn patterns. 236.080000 240.240000 They build a statistical model of language, a system that can guess 240.240000 245.520000 what's most likely to come next based on what it's seen before. 245.520000 248.160000 So let's take a look at an example. 248.160000 253.760000 If I say the words once upon a, you're probably thinking time. 253.760000 255.280000 That's prediction. 255.280000 257.280000 That's pattern recognition. 257.280000 263.040000 That's exactly what LLMs do, just at a far more sophisticated level. 263.040000 267.600000 You feed it a prompt and it continues based on probability. 267.600000 271.440000 And because it's trained on so much human written content, 271.440000 277.280000 the outputs it generates often sound surprisingly natural or even creative. 277.280000 281.120000 It can write emails, summarize documents, answer questions, 281.120000 285.840000 brainstorm ideas, and mimic a variety of tones or formats. 285.840000 291.360000 All of this is possible because it has statistically learned the structure, 291.360000 293.600000 rhythm, and nuance of language. 293.600000 299.520000 So when someone says we're using an LLM, what they really mean is, 299.520000 305.200000 we're using a highly advanced system that guesses what comes next in the conversation 305.200000 308.160000 or a piece of writing with accuracy. 308.160000 313.200000 And truthfully, nobody's probably going to say I'm using an LLM. 313.200000 316.720000 They'll most likely say I'm using AI, but I digress. 316.720000 323.280000 The guessing game as simple as it sounds is the foundation of some of the most powerful 323.280000 326.400000 and disruptive technology we've seen in a generation. 326.400000 333.440000 Let's keep going and break down exactly how these models handle that process under the hood 333.440000 337.280000 with language building blocks or more specifically tokens. 337.280000 345.200000 Now, if LLMs are built on predicting the next piece of text, what exactly are they predicting? 345.200000 349.360000 Not letters, not full words, but tokens. 349.360000 355.200000 A token is usually a chunk of a word, something like a syllable or a root. 355.200000 358.880000 For instance, take the word unbelievable. 358.880000 362.560000 Most models would break it down into several tokens. 363.520000 366.800000 Unbelieve and a bull. 366.800000 369.120000 So why does this matter? 369.120000 371.680000 Because language is flexible. 371.680000 373.440000 People write informally. 373.440000 375.200000 They misspell things. 375.200000 376.960000 They mash words together. 376.960000 381.280000 And a lot of language, especially on the internet, isn't clean or polished. 381.280000 387.040000 Tokenizing language into smaller units lets the model handle all of them. 387.040000 390.400000 Think of tokens like legal bricks. 391.360000 393.440000 Full words are too big and bulky. 393.440000 398.240000 But with tokens, the model can snap ideas together at a finer resolution. 398.240000 403.120000 That gives it more flexibility in understanding and generating language. 403.120000 406.240000 Now, here's where it gets really important. 406.240000 412.400000 Every model has a token limit, a cap on how many tokens it can consider at once. 412.400000 416.800000 Think of it like the model's short-term memory or its attention span. 418.240000 427.280000 Chat GPT40 or GPT4 Turbo, for example, has a context window of 128,000 tokens. 427.280000 431.760000 It's massive, roughly the equivalent of a 300-page book. 431.760000 438.160000 It means the model can absorb and reason over a huge chunk of information all at once. 438.160000 446.240000 Documentation, emails, transcripts, code, even multiple conversations, all in one session. 447.200000 451.680000 But once you exceed that limit, older parts of the input are dropped or ignored. 451.680000 457.200000 So if you're feeding in long documents or having extended interactions, 457.200000 460.560000 you're literally working within the bounds of a memory ceiling. 460.560000 465.680000 This has real implications for everything, from customer service workflows, 465.680000 469.440000 to legal research, to internal knowledge bots. 470.880000 477.280000 If you've ever used Chat GPT and found that it's forgot what you said earlier in a conversation, 477.280000 480.400000 it's probably because you exceeded the token limit. 480.400000 486.560000 It's not being lazy, it just doesn't have enough room left in its memory to hold onto everything. 486.560000 491.920000 And this token limit matters a lot for enterprise use cases. 491.920000 498.560000 If you're summarizing a long report, analyzing contracts, or feeding in a bunch of documentation, 499.120000 501.760000 you need to know whether the model can handle that volume. 501.760000 508.000000 Some models truncate input, others drop the earliest parts of the conversation, 508.000000 514.480000 and some may split documents up automatically, sometimes losing key context in the process. 514.480000 519.680000 Understanding tokens gives you a sense of the model's constraints. 519.680000 524.640000 It tells you how much you can throw at it and how much it can retain at once. 525.680000 532.000000 And when you're evaluating tools or vendors, knowing the token limit can save you a lot of frustration 532.000000 535.040000 and help you make better architectural decisions. 535.040000 541.600000 Now that we understand tokens, let's demystify the transformer. 541.600000 544.560000 And no, we're not talking about Autobots or Decepticons. 544.560000 549.760000 If you've ever wondered how today's AI models can carry on a conversation, 549.760000 554.240000 rightful paragraphs, or handle translations that actually make sense, 554.960000 558.240000 it all goes back to a breakthrough in 2017. 558.240000 565.200000 That year, a team of researchers at Google published a paper titled "Attention is All You Need." 565.200000 571.680000 In that paper, they introduced a new neural network architecture called the "Transformer," 571.680000 575.840000 and it completely changed the game for natural language processing. 575.840000 581.440000 Prior to transformers, most AI models processed text sequentially, 582.000000 585.680000 reading one word after another, in order, like a typewriter. 585.680000 591.840000 That worked fine for short phrases, but it fell apart with long or complex sentences. 591.840000 597.760000 These models struggled to remember or relate words that were far apart in a sentence. 597.760000 604.240000 The transformer architecture solved that problem using something called "self-attention." 604.240000 610.080000 This mechanism allows the model to weigh the importance of different words in a sentence 610.080000 612.720000 or a paragraph, regardless of their position. 612.720000 615.680000 So let's look at another real example. 615.680000 621.040000 Take the sentence, "She didn't like the movie because it was too long." 621.040000 627.040000 If you ask, "What does it refer to?" you'd say, "The movie," right? 627.040000 630.800000 Older models struggled with that kind of reference, 630.800000 639.520000 but transformers can understand that "it" relates to "movie," even though the two words aren't side-by-side. 640.240000 645.440000 That's because "self-attention" lets the model compare every word with every other word, 645.440000 647.760000 scoring the relationships between them. 647.760000 654.080000 It's kind of like giving the model a highlighter and saying, "Focus here, these are the important bits." 654.080000 662.560000 This ability to understand context across the entire input is why transformers are so powerful. 663.520000 670.880000 It's also why today's chatbots can actually stay on topic, why AI tools can summarize reports or 670.880000 677.200000 meaning notes in a way that makes sense, and why models can now handle tasks like translation, 677.200000 682.160000 writing, or basic analysis with results that are often surprisingly useful. 682.160000 686.480000 Transformers are the core engine behind all of that. 687.440000 694.320000 They're the reason models like GPT, 4.0, Claude, and others can understand relationships and language. 694.320000 699.040000 They can respond in full sentences and adapt to the context of what you're saying. 699.040000 705.600000 Without transformers, this level of natural conversation and responsiveness wouldn't be possible. 705.600000 712.400000 And this also ties back to what I shared previously. The token limit is the memory size and the 712.400000 717.760000 transformer is how the model navigates and prioritizes everything in that memory. 717.760000 725.760000 So if tokens are the bricks, the transformer is the architect deciding how they all fit together. 725.760000 733.760000 All right, so now let's talk about the concept of embeddings. This is how the model represents meaning. 733.760000 740.880000 And while that word might sound a little philosophical, this part is actually very mathematical. 741.760000 745.440000 The model doesn't understand words the way you and I do. 745.440000 750.560000 It doesn't know that a dog is furry or that it chases tennis balls. 750.560000 756.560000 Instead, it recognizes patterns from how words appear together across millions of documents. 756.560000 766.320000 So it might learn the word dog often shows up near other words like walk or bark. Puppy, leash, 766.320000 772.720000 you get the idea. The way it captures these relationships is through something called embeddings. 772.720000 780.320000 Embeddings are just numbers, specifically vectors. Think of them like GPS coordinates. 780.320000 787.360000 Each word, phrase, or even entire sentence gets plotted in a multi-dimensional space. 787.360000 795.440000 Words that are used in similar ways like doctor and nurse or Monday and Tuesday end up 795.440000 802.640000 close together in that space. So if you ask the model, what's the capital of Italy? It doesn't pull 802.640000 810.320000 out a flashcard labeled Rome. It looks at how the phrase capital of relates to countries in its 810.320000 816.400000 internal map and figures out that Rome is the most statistically likely answer in a neighborhood 816.400000 824.400000 around Italy. So let's take a look at another example here. In some early embedding experiments, 824.400000 831.680000 researchers found that you could do math with words. You could take the vector for king, subtract the 831.680000 840.880000 vector for man, then add the vector for woman, and get something surprisingly close to the vector for 840.880000 849.120000 queen. That kind of relationship math is what makes embeddings so powerful. It gives the model a 849.120000 856.880000 sense of analogy, similarity and context. So whether you're feeding it legal contracts, customer 856.880000 863.040000 complaints, or help desk tickets, the model is processing that information by plotting it all into 863.040000 870.880000 this high dimensional space and navigating that map when it responds. Embeddings are used in 870.880000 878.800000 search, classification, recommendation engines, and especially in fine tuning and retrieval 879.040000 886.000000 augmented generation systems. In other words, they're the hidden geometry behind how the model 886.000000 894.080000 thinks. Moving right along, let's talk about model scale and why more isn't always the best option. 894.080000 900.800000 When we talk about large language models, we're referring to systems with billions or even 900.800000 907.600000 trillions of parameters. Parameters are like tiny adjustable dials that the model tunes during 907.600000 915.440000 training to help it better predict the next word or token. GPT-4, for example, has over a trillion 915.440000 921.600000 of these parameters. It's a big part of what makes it so capable. It's been trained on vast 921.600000 928.080000 amounts of text and has the complexity to handle all sorts of tasks with a high level of accuracy. 928.080000 935.520000 But a larger model isn't always the most practical choice. Larger models often come with trade-offs. 936.160000 943.280000 They can be slower to respond. They can be more expensive to run. They require more powerful hardware 943.280000 950.000000 and may be harder to deploy in constrained environments. Depending on your needs, 950.000000 957.760000 a smaller model like GPT-3.5, Claude Instant, or an open source alternative, like Mistral, 957.760000 964.560000 might be faster, more cost-effective, and easier to integrate. And with the right approach, 964.560000 969.360000 you can often close the capability gap between smaller and larger models. 969.360000 976.400000 That's where retrieval, augmented generation, or RAG, comes in. 976.400000 983.040000 Rather than asking the model to rely on what it learned during training, RAG lets you connect 983.040000 989.280000 it to live external data, like your own documents, knowledge bases, or internal systems. 990.640000 995.840000 So if you ask a question about your company's onboarding process, it can retrieve the exact 995.840000 1002.800000 information from your documentation and generate a tailored answer. Think of it like giving the model 1002.800000 1010.080000 real-time access to your organization's library. This setup gives you the responsiveness and affordability 1010.080000 1015.360000 of a smaller model, with the context and relevance you'd expect from a much more powerful one. 1016.400000 1022.240000 In the end, the best model isn't necessarily the biggest. It's the one that fits your use case, 1022.240000 1030.160000 your data, and your operational goals. So what does this all mean for you? It means large language 1030.160000 1036.720000 models are not magic. They're not aware or conscious, they're not doing research, and they definitely 1036.720000 1042.880000 don't know anything in a traditional sense. But they are incredibly good at pattern recognition. 1043.440000 1049.360000 They've seen so much text, so many examples, and so many ways we as humans use language 1049.360000 1054.800000 that they can mimic a wide range of human communication with impressive fluency. 1054.800000 1061.680000 That makes them useful across almost every department. In customer service, they can 1061.680000 1068.800000 power chatbots and help desks. In HR, they can help draft job descriptions or onboarding material. 1069.520000 1076.160000 In legal, they can summarize documents or highlight key risks. In IT, they can write code snippets 1076.160000 1082.480000 or help troubleshoot errors. In marketing, they can generate social posts or even landing page ideas. 1082.480000 1088.160000 They're already showing up in products you use every day, like email clients that draft replies, 1088.160000 1097.120000 office tools that suggest edits, or apps that summarize meetings. But, and this is a big but, 1097.120000 1103.280000 they also can hallucinate. They can make things up, especially when asked about things outside 1103.280000 1110.800000 their training data or when trying to sound confident. So if you're adopting LLMs in your organization, 1110.800000 1118.320000 treat them like very capable interns. They're fast, tireless, and surprisingly creative, but they 1118.320000 1125.920000 still need review. Understanding how these models work gives you a huge advantage. You can ask 1125.920000 1131.840000 smarter questions when evaluating tools. You'll know when to trust the output and when not to. 1131.840000 1138.640000 You can better assess AI risks from data leakage to compliance to reputational damage. 1138.640000 1147.440000 And you can help your team focus on real value, not hype. In short, this is your moment to lead with 1147.440000 1155.280000 clarity. Not by becoming an AI expert, but by becoming an AI literate decision maker who knows 1155.280000 1160.800000 how to separate the signal from the noise. All right, let's bring it all together. 1160.800000 1167.040000 Large language models are powerful tools, but only if you understand what they are and what they're 1167.040000 1172.800000 not. They're not search engines, they're not fact checkers, and they're definitely not self-aware. 1172.800000 1178.800000 They're incredibly advanced systems that generate text based on patterns they've learned from massive 1178.800000 1185.200000 amounts of data. They don't know the meaning behind the words. They just get very good at putting 1185.200000 1192.400000 the right ones in the right order. We've unpacked a lot in this episode. What LLMs are really doing 1192.400000 1198.800000 under the hood, how tokens and transformers work together, why embeddings give them a sense of 1198.800000 1206.240000 relationship, and how a model size, memory capacity, and internal design influence the kinds of tasks 1206.240000 1212.080000 it can handle well. More importantly, I've talked about how to apply that knowledge in a business 1212.080000 1217.760000 context, so you're not just going along with the height, but making smarter decisions about how 1217.760000 1224.240000 and where to use AI. If you're a tech leader, a department head, or even just someone trying to 1224.240000 1230.720000 stay sharp, this kind of understanding gives you an edge. If this episode give you clarity or sparked 1230.720000 1236.240000 ideas, consider sharing it with someone in your circle. And if you have a question or want a deeper 1236.240000 1243.040000 dive on a specific AI topic, let me know. I've got more episodes on the way. This is the Tech It From Me podcast. Thank you for listening. Tech It From Me is an independent and solo-produced podcast.