How Llms Process Language

Ever wondered how Large Language Models (LLMs) like ChatGPT actually learn to talk like humans? It all comes down to a multi-stage training process - from raw data learning to human feedback fine-tuning. Here’s a quick breakdown of the 4 Stages of LLM Training: Stage 0: Untrained LLM At this stage, the model produces random outputs — it has no understanding of language yet. Stage 1: Pre-training The model learns from massive text datasets, recognizing language patterns and structure - but it’s still not conversational. Stage 2: Instruction Fine-Tuning Now, it’s trained on question–answer pairs to follow instructions and provide more useful, context-aware responses. Stage 3: Reinforcement Learning from Human Feedback (RLHF) The model learns to rank responses based on human preference, improving response quality and helpfulness. Stage 4: Reasoning Fine-Tuning Finally, the model is trained on reasoning and logic tasks, refining its ability to produce factual and well-structured answers. Understanding how LLMs evolve helps you build, prompt, and use them better.

Tokenization – The Root of All Evils? 🔢👿 Tokenization is the first step in how language models process text. The translation layer between human-readable text and numbers that computers can process. Why can't we just convert each letter to a number❓ Two critical problems: ❌ Wasting massive computing power relearning basic character patterns like "th" or "ing" repeatedly. ❌ Long sequences of individual characters make it nearly impossible for the model to learn meaningful language patterns. This is why modern LLMs use Byte-Pair Encoding (BPE). 💡 BPE combines common character patterns into single tokens. Instead of seeing: "i" "c" "e" "space" "c" "r" "e" "a" "m", the AI sees: "ice" " cream" (note the space at the beginning of the token). This unlocks modern LLMs capabilities, but creates three major flaws: 🔢 Arithmetic ▪️ Historically significant numbers (like years 1930-2019) might get single tokens, while other numbers don't. This makes it very hard for models to learn basic arithmetic. 🍓 Word-Counting ▪️ Since words are broken into chunks rather than individual letters, LLMs struggle with counting 'r' in "strawberry". 🌍 Languages ▪️ Less common languages often get suboptimal tokenization since their patterns appear less in training data. Tokenization is the great paradox of LLMs. ⚖️ Their fundamental enabler also limits what they can achieve. Current research seems to find higher ROI in other improvements, this limitation remains. The future might lie in a routing approach - different processing methods for different tasks, rather than forcing everything through the same tokenization pipeline. 💡 At least, that’s how I see it—what about you?

I like using this completion probabilities visualization tool with my team to help them understand how LLMs work in practice. It’s a bit technical, but it does a great job of visually breaking down the whole LLM stack and showing how LLMs process and generate responses. The tool lets you visualize the probability distribution of the completions (~words). In the video, I walked through a few examples to show how the probabilities change with different contexts, here are some insights: 1/ Models don’t generate words randomly. They calculate likelihoods based on training data and context. For example, if you prompt with "What is the best project management tool?", the model predicts possible completions based on probability. The highest-ranked options might include "Trello", "Asana", or "Jira", with each word’s likelihood depending on past training data. Once the model commits to the first letter, the probabilities narrow dramatically. If it starts with "T", it’s likely completing with "Trello". If it starts with "A", it’s probably "Asana". The initial probability distribution shifts based on the wording of the prompt and any additional context, like previous user or system instructions. 2/ Context changes probabilities. The model continuously updates probabilities based on the preceding text. If specific words or phrases appear earlier in the prompt, they influence which words are more likely to be selected next. Even minor changes in wording or structure can shift the probability distribution. 3/ This applies to search, RAG, and prompt engineering. RAG modifies token probabilities by injecting external information before the model generates a response. Retrieved snippets affect which words are predicted by reinforcing certain completions over others. When no external data is used, the model relies solely on its training data distribution. This highlights how small tweaks in wording, context, or retrieved content can significantly influence AI-generated responses. If you're optimizing for AI search, you should consider these factors in shaping what gets surfaced. I’ll dive deeper into how to optimize for them in upcoming posts. This is part of my AI Optimization Series, where I break down how LLMs process information and how to adapt content for AI search. You can check my two previous posts in this series here. How big is AI search: [https://lnkd.in/eNUidXtg] How AI is transforming how we get information [https://lnkd.in/e7WPd_2t]

LLM explained like a 10 year old ! I think these are going to become like a series. Imagine a super-smart robot that has read almost everything—books, websites, news articles, Wikipedia, even Reddit threads. It doesn’t “think” like we do and doesn’t really understand the world. But it’s extremely good at figuring out which words go together—like a master at language puzzles. That robot is what we call a Large Language Model, or LLM. ⸻ So how does it work? LLMs are trained by reading billions of words and learning patterns. They don’t memorize facts—they learn how language works. Here’s a simplified breakdown: 1. Training: First, they’re fed huge amounts of text—books, websites, articles. The model learns by guessing the next word in a sentence, over and over again. If it sees “The sun rises in the ___,” it learns that “morning” is a good guess. 2. Neural Networks: Under the hood, they use something called a neural network—a type of algorithm inspired by how our brains work. But instead of neurons, it uses math and probabilities to make decisions. 3. Tokens and Context: The model doesn’t read full paragraphs like we do—it breaks everything into small pieces (called tokens) and analyzes them in chunks, using context to figure out the most likely next word. 4. Fine-tuning: After training, the model can be fine-tuned for specific industries or tasks—like legal analysis, customer service, or medical Q&A. 5. Prompting: When you interact with it (e.g., ChatGPT), you’re sending it a prompt. The model scans the prompt and predicts what comes next—word by word—based on what it’s learned. It doesn’t “know” anything, but it’s astonishingly good at sounding like it does, because it’s drawing on patterns across everything it’s ever read. ⸻ What are LLMs good at? • Writing and summarizing text (emails, blogs, documents, even code). • Drafting customer responses or internal knowledge answers. • Parsing unstructured data like PDFs, emails, chats, and logs. • Brainstorming, prototyping, and assisting with repetitive tasks. ⸻ What they’re not great at: • Factual accuracy: They can “hallucinate”—make up wrong but confident-sounding answers. • Reasoning across steps: Logic and math aren’t their strengths without help. • Understanding the real world: They don’t know what’s true—they only know what’s likely based on the text they’ve seen. • Current events: Unless connected to live data, they don’t know what happened yesterday. • Judgment: They don’t have common sense, intent, or ethics—they mimic language, not thinking. ⸻ So why do they matter? Because LLMs let us interact with computers in natural language—and that’s a game-changer. They’re not magic, but they are powerful tools when paired with the right data, governance, and human oversight. #AI #LLM #ChatGPT #ArtificialIntelligence #ResponsibleAI #DigitalTransformation #Innovation

What truly powers AI agents? Agentic workflows may be the buzzword of the moment, but let’s take a step back and revisit the foundation: Large Language Models (LLMs). How does an LLM actually learn? Here’s a simplified breakdown of the three key phases: 1️⃣ Self-Supervised Learning (Understanding Language) LLMs are trained on massive text datasets (e.g., Wikipedia, blogs, websites). They use transformer architectures to predict the next word in a sequence. Example: “A flash flood watch will be in effect all ___.” The model ranks possible answers like “night” or “day” and improves over time. 2️⃣ Supervised Learning (Understanding Instructions) At this stage, the model is fine-tuned using examples of questions and ideal responses. It learns to align with human preferences, making its answers more relevant and accurate. 3️⃣ Reinforcement Learning (Improving Behavior) Feedback from humans (e.g., thumbs up/down ratings) helps refine the model. This ensures the model avoids harmful outputs and focuses on being helpful, honest, and safe. How do LLMs generate responses? When you ask a question, the model: Breaks it into tokens (small text segments turned into numbers). Processes these tokens through neural networks to predict the best response. Handles a token limit, meaning it can “forget” earlier context if the input exceeds this limit. Two key components of an LLM: Parameter File: A compressed repository of the model’s knowledge. Run File: Instructions for using the parameter file, including tokenization and response generation. These foundational models are the backbone of AI agents. While workflows evolve, understanding LLMs is crucial to grasp the bigger picture of AI. Let’s not lose sight of what makes these innovations possible!

Having the ability to clearly explain fundamental concepts in AI to others is incredibly important. To explain large language models (LLMs), I use a simple three-part framework… Why is this important? Given that most AI engineers/researchers work on teams with highly-technical members, they might not get a lot of opportunities to explain concepts like the transformer architecture or alignment to those who are non-technical. However, such an ability is incredibly important as political leaders are crafting legislation for AI and AI-powered tools are becoming more widely utilized in the broader public. (1) Transformer: Modern language model are based upon the transformer architecture—a type of deep neural network that can text as input and produce text as output. The transformer has two components—encoder and decoder—but LLMs use a decoder-only architecture, which only has a decoder. This model takes a textual sequence as input and repeatedly performs two operations: - Masked self-attention: each word looks at prior words in the sequence. - Feed-forward transformation: each word is individually transformed. Together, these two operations allow the transformation to learn meaningful relationships between words across an entire sequence of text to produce the correct textual output. (2) Pretraining: All language models rely upon the next word/token prediction objective at their core. This objective is quite simple! Given a large corpus of text downloaded from the web, we just train the LLM by: 1. Sampling some text from the corpus. 2. Ingesting the text sequence with the decoder-only transformer. 3. Training the model to correctly predict each word in the sequence given preceding words as input. This self-supervised objective works great for pretraining LLMs, as we can efficiently train the model over a large amount of unlabeled text, allowing the LLM to amass a large knowledge base. Extra tip for Pretraining: Next word/token prediction is also used to generate text with an LLM. Starting with an input sequence (i.e., the prompt), we just continually predict the next word, add it to the input sequence, predict the next word, and so on. (3) Alignment: Pretraining teaches the LLM to be really good at predicting the most likely next word, given preceding words as input. But, what we actually want is an LLM that produces and interesting and useful output. For this, we need to align the model, or train it in a way that encourages it to generate outputs that better align with the desires of a human user (according to a set of predefined alignment criteria). To do this, we use to finetuning techniques: - Supervised finetuning (SFT): finetune the model on examples of desirable outputs. - Reinforcement Learning from Human Feedback (RLHF): finetune the model on pairs of model outputs, where the “better” output is ranked by a human annotator.

Most people assume large language models are like search engines or knowledge bases. They’re not. LLMs are stochastic text generators. That means: • They don’t store facts. • They don’t understand meaning. • They don’t retrieve answers from a database. Instead, they predict the most likely next word, one token at a time, based on the patterns they’ve seen in massive text datasets. This process is inherently probabilistic. The model doesn’t always give the same output. You can actually set a parameter called temperature to make it more or less “random.” Lower temperature = more deterministic. Higher temperature = more creative or chaotic. So when an LLM gives you: • A brilliant summary of a legal document • A wrong answer to a basic math question • A hallucinated source that doesn’t exist …it’s not being lazy. It’s doing exactly what it was trained to do: generate fluent, likely-sounding language. This doesn’t make LLMs useless. It just means we need to treat them as stochastic tools, not deterministic ones. And that’s why smart builders wrap LLMs with: • Prompting patterns (like chain-of-thought reasoning) • Retrieval (so the model can pull in factual context) • Post-processing (to catch or correct hallucinations) LLMs aren’t broken. They’re just uncertain by design Follow me for more clear, no-hype explanations of how this space is evolving. #LLMs #AIExplained #PromptEngineering #GenerativeAI #NLP #LanguageModels #AppliedAI

Inside the Mind of an LLM: What Every CEO and Board Must Know AI is no longer experimental—it’s a strategic weapon. And at the heart of this transformation sits the Large Language Model (LLM). But how do LLMs work? And why should the C-suite care? Let’s break it down. ⸻ 🔍 How LLMs Work (in Plain English) LLMs are built on neural networks—math models inspired by the brain. They’re trained on trillions of words from books, code, and the web to recognize patterns in language and meaning. The process: 1. Tokenization – Input text is split into small units called “tokens. 2. Embedding – Each token is converted into a numeric vector representing its meaning. 3. Transformer Architecture – The core engine. It uses “attention” to understand which words relate to each other across a sentence, paragraph, or page. 4. Prediction – The model then generates the most likely next word—over and over—resulting in smart, coherent output. This is how LLMs summarize reports, answer complex questions, write code, and power enterprise tools in real time. ⸻ 🏢 Why It Matters for Enterprises LLMs aren’t just IT tools—they’re a new layer of intelligence. They’re changing: • Customer service → instant, human-like support • Finance → real-time analytics and reporting • Engineering → AI-assisted code generation • Legal & compliance → document summarization and risk detection They are accelerating productivity, reducing costs, and creating new growth levers. ⸻ 🌍 Wider Societal Impact LLMs are reshaping: • Education → personal AI tutors • Healthcare → automated medical summaries • Accessibility → language translation and speech-to-text Knowledge work is being redefined. ⸻ 📈 What Boards and CEOs Must Do 1. Build AI Literacy – You don’t need to code, but you must understand what LLMs can do. 2. Fix the Data Foundation – Clean, connected data is critical. 3. Invest in Guardrails – Accuracy, bias, and governance must be built in. 4. Align to Business Value – Every deployment should target cost, speed, or growth. 5. Redesign Workflows – AI isn’t a sidecar. It’s a co-pilot for your entire organization. ⸻ 💬 Final Word: This isn’t just a tech shift. It’s a paradigm shift. The question isn’t “Should we use LLMs?” It’s “How do we lead with them?” #AI #LLM #EnterpriseAI #DigitalTransformation #CEO #BoardLeadership #NeuralNetworks #AgenticAI

This is hands down one of the best visualizations of how LLMs actually work. Let's break it down: 1 - Tokenization and embeddings: - The input text is split into tokens, smaller chunks that are not always whole words. - Each token is mapped to a vector in high-dimensional space, where words with similar meanings sit close together. 2 - The attention mechanism (self-attention): Words update each other based on context, so "bank" in riverbank does not get confused with the financial kind. The attention block weighs the relationships between words and refines their representations as it goes. 3 - Feed-forward layers (deep neural network processing): After attention, the tokens pass through feed-forward layers that refine the meaning a little more. Each layer picks up deeper semantic relationships, which sharpens the prediction. 4 - Iteration and deep learning: This repeats through dozens or even hundreds of layers, adjusting the token meanings step by step. That is where the "deep" in deep learning comes in: layers upon layers of matrix multiplications and optimizations. 5 - Prediction and sampling: The final vector becomes a probability distribution over the next word. The model samples from that distribution and generates the text one word at a time. These mechanics sit at the core of every LLM, ChatGPT included. If you want to build scalable, responsible AI solutions, it pays to actually understand how they work rather than treating the model as a black box. Here is the full video from 3Blue1Brown. I'd read, watch, and bookmark it for a proper deep dive: https://lnkd.in/dAviqK_6 Enjoy. ↓ 𝗜𝗳 𝗔𝗜 𝗶𝘀 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯, 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝗶𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂. 𝗜 𝘀𝗵𝗮𝗿𝗲 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝘁𝗼𝗼𝗹𝘀, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁’𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗲𝗹𝗱: https://lnkd.in/dbf74Y9E

How Do Large Language Models Work? The diagram below illustrates the core architecture of LLMs. Step 1: Tokenization The LLM breaks down text into manageable units called tokens. It handles words, subwords, or characters using techniques like BPE, WordPiece, or SentencePiece. This process transforms natural language into token IDs that the model can process, with special tokens marking the beginning, end, or special functions within the text. Vocabulary size and token compression techniques are crucial for efficient processing. Step 2: Embedding This layer transforms discrete token IDs into rich vector representations in a high-dimensional semantic space. It combines word vectors with positional encoding to preserve sequence information. The embedding matrix captures semantic relationships between words, allowing similar concepts to exist near each other in the vector space. Step 3: Attention The heart of modern LLMs, attention determines which parts of the input to focus on when generating each output token. Using query, key, and value vectors, it computes relevance scores between all tokens in the sequence. Multi-head attention processes information in parallel across different representation subspaces, capturing various relationships simultaneously. Self-attention allows the model to consider the entire context when processing each token. Step 4: Feed-Forward This component transforms each token's representation independently through a multi-layer perceptron (MLP). It applies non-linear activation functions like GELU or ReLU to introduce complexity that captures subtle patterns in the data. The feed-forward network increases the model's capacity to represent complex functions and relationships. It processes token representations individually, complementing the contextual processing of the attention mechanism. Step 5: Normalisation Layer normalisation standardises inputs across features, while residual connections allow information to flow directly through the network. Pre-norm and post-norm architectures offer different stability-performance tradeoffs. Dropout prevents overfitting by randomly deactivating neurons during training, forcing the model to develop redundant representations. Step 6: Prediction The final step transforms the processed representations into probabilities over the vocabulary. It generates logits (raw scores) for each possible next token, which are converted to probabilities using the softmax function. Temperature sampling controls randomness in generation, with lower temperatures producing more deterministic outputs. Decoding strategies like greedy, beam search, or nucleus sampling determine how the model selects tokens during generation. What makes LLMs different from traditional language processing systems is their autoregressive nature. This creates a step-by-step generation process rather than producing entire responses at once. In your view: Which architectural component causes hallucinations in LLMs?