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

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?

𝐄𝐯𝐞𝐫 𝐖𝐨𝐧𝐝𝐞𝐫𝐞𝐝 𝐇𝐨𝐰 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐓𝐡𝐢𝐧𝐤𝐬?  Here is the Answer Large Language Models (LLMs) like GPT are revolutionizing how we interact with AI.  But how do they actually work? Here is a breakdown of the process: 𝟏. 𝐈𝐧𝐩𝐮𝐭: • The journey starts with training text from books, websites, and conversations.  • The model processes your user prompt alongside context memory (previous interactions) to understand the task. • Tokenization breaks text into smaller, manageable pieces (tokens), and settings & rules ensure the model operates within safe boundaries. 𝟐. 𝐋𝐋𝐌 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: • The model generates embeddings, turning tokens into meaningful numbers it can work with. • Self-attention lets the model understand the relationships between words. • Transformer layers handle deep neural network reasoning, with pre-training knowledge from vast data sources and fine-tuning to ensure relevant, safe answers. 𝟑. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧: • The core of LLMs is next-token guessing, predicting the next word based on context. • Decoding methods control how the output is structured, while sentence building ensures coherence. • Safety checks are in place to filter harmful responses, and the repeat loop ensures the model keeps working until the answer is complete. 𝟒. 𝐎𝐮𝐭𝐩𝐮𝐭: • The model produces a final response-an answer, code snippet, or summary-with clean formatting to make it easy to read. • Optional tools like retrieval systems or external APIs enhance the response, while user feedback helps the model learn and improve. LLMs are built on a complex, multi-step process that combines cutting-edge technology with continuous learning to deliver meaningful, human-like responses. ♻️ Repost this to help your network ➕ Follow Prashant Rathi for more insights on Enterprise AI PS. Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently McKinsey & Company) or affiliates. #GenAI #LLM #AgenticAI

How LLMs See the World (Explained Simply) Most people think ChatGPT reads text like we do. It doesn’t. When you type “Hello world”, the model doesn’t see letters. It turns everything into numbers because numbers are what it can understand. Here’s the simple version 👇 1️⃣ First: Clean the text The system fixes spacing, symbols, and formatting. It makes your text neat and consistent so the model can work with it. 2️⃣ Next: Break the text into pieces LLMs don’t read full words. They break text into mini-pieces called tokens. Modern AI (GPT, Claude, Gemini) uses subword tokens small chunks that help the model understand both common and unusual words. So “Hello world” might get split into: ["Hell", "o", "world"] This step is crucial  it shapes how the model interprets your meaning. 3️⃣ Finally: Turn each piece into numbers Each token becomes a number, like: [15496, 345, 995] And that’s what the model actually sees. Those numbers get mapped into its internal “meaning space” where it compares patterns and relationships. LLMs don’t understand English directly. They understand patterns in numbers that represent language. Why this matters This affects: • how well your prompts work • why wording changes the output • why some models handle code better • how fast a model processes info • how much text it can remember at once If you understand how LLMs see, you can make them work better for you, whether you’re writing emails, building workflows, or making business decisions. 🔁 Repost to help more people finally understand what’s happening under the hood. ➕ Follow Gabriel Millien for simple, clear explanations about LLMs and the future of AI.