How Large Language Models Create Text Responses

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

Large Language Models (LLMs) are immensely complex Machine Learning systems, trained on the text of the entire internet, capable of generating plausible text responses to prompts. Popularized in November 2022 by OpenAI's ChatGPT, LLMs have created a wave of excitement, investment, and hype greater than any technology that I have ever seen in my 40-year career. Over 86,000 people viewed my recent post, "The Human Brain Is Not a Large Language Model". It seems that there is a widespread desire to understand LLMs, but it is hard to find friendly explanations that non-technical people can understand and relate to. So, here is my friendly yet authoritative explanation of a Large Language Model. LLMs are based on Transformers, which were first described in a famous paper, "Attention Is All You Need" in 2017 by researchers at Google. The famous block diagram of the Transformer is shown below, on the left. Unfortunately, this paper was really about Machine Translation, so the block diagram is not what is used in a modern LLM like ChatGPT or Llama-2 or Llama-3. You have to know about it, but don't use it. In the middle diagram below, I am showing a good block diagram of a Modern LLM. This diagram of a Decoder-Only Transformer was originally published by Umar Jamil, and I have added the Sampler (small purple block at the top) and Auto-Regressor (wire feeding the output back to the next input). Umar has a beautiful 70-minute video explaining how the Decoder-Only Transformer works in Llama-2. I made a nice 28-minute video too. We discuss the Embeddings, Multi-layer architecture, Self-Attention Blocks, Key-Value Cache, Layer Normalization, Rotary Positional Encoding, and final output of Next Token Probabilities. Links in the first comment. You can use this diagram as a complete abbreviated summary of how an LLM works. Finally, on the right, we have a friendly top-level summary of the middle diagram. We are showing all the complexity of the Decoder-Only Transformer in a single block called the Next Token Probability Distribution Predictor. Think of that block as the Billion-Dollar Machine. All that fancy machinery is just looking at the recent tokens, and producing a list of candidate next tokens and their probabilities. The colorful Pie Chart shows the candidate next tokens for the prompt "Why is the sky blue?". The next token could be "\n" (new line), "The", "What", "Why", or other less likely tokens. The Sampler is a Random Number Generator that produces a number between 0 and 1, used to choose the Next Token from the candidates in the Pie Chart. The Billion-Dollar Machine makes the Pie Chart of candidate Next Tokens, and the Sampler is like a Dart-Throwing Monkey, making the executive decision about which token to produce next. Finally, the Auto-Regressor feeds this output token back to the input of the Billion-Dollar Machine, to start the process over again for another new token. (continued briefly in first comment)

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.

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

Large Language Models (LLMs) are powerful, but how we 𝗮𝘂𝗴𝗺𝗲𝗻𝘁, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 them truly defines their impact. Here's a simple yet powerful breakdown of how AI systems are evolving: 𝟭. 𝗟𝗟𝗠 (𝗕𝗮𝘀𝗶𝗰 𝗣𝗿𝗼𝗺𝗽𝘁 → 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲)   ↳ This is where it all started. You give a prompt, and the model predicts the next tokens. It's useful — but limited. No memory. No tools. Just raw prediction. 𝟮. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻)   ↳ A significant leap forward. Instead of relying only on the LLM’s training, we 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗳𝗿𝗼𝗺 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 (like vector databases). The model then crafts a much more relevant, grounded response.   This is the backbone of many current AI search and chatbot applications. 𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗟𝗠𝘀 (𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 + 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲)   ↳ Now we’re entering a new era. Agent-based systems don’t just answer — they think, plan, retrieve, loop, and act.   They: - Use 𝘁𝗼𝗼𝗹𝘀 (APIs, search, code) - Access 𝗺𝗲𝗺𝗼𝗿𝘆 - Apply 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗰𝗵𝗮𝗶𝗻𝘀 - And most importantly, 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗱𝗼 𝗻𝗲𝘅𝘁 These architectures are foundational for building 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀, 𝗰𝗼𝗽𝗶𝗹𝗼𝘁𝘀, 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗲𝗿𝘀. The future is not just about 𝘸𝘩𝘢𝘵 the model knows, but 𝘩𝘰𝘸 it operates. If you're building in this space — RAG and Agent architectures are where the real innovation is happening.

If you’re an AI engineer, understanding how LLMs are trained and aligned is essential for building high-performance, reliable AI systems. Most large language models follow a 3-step training procedure: Step 1: Pretraining → Goal: Learn general-purpose language representations. → Method: Self-supervised learning on massive unlabeled text corpora (e.g., next-token prediction). → Output: A pretrained LLM, rich in linguistic and factual knowledge but not grounded in human preferences. → Cost: Extremely high (billions of tokens, trillions of FLOPs). → Pretraining is still centralized within a few labs due to the scale required (e.g., Meta, Google DeepMind, OpenAI), but open-weight models like LLaMA 4, DeepSeek V3, and Qwen 3 are making this more accessible. Step 2: Finetuning (Two Common Approaches) → 2a: Full-Parameter Finetuning - Updates all weights of the pretrained model. - Requires significant GPU memory and compute. - Best for scenarios where the model needs deep adaptation to a new domain or task. - Used for: Instruction-following, multilingual adaptation, industry-specific models. - Cons: Expensive, storage-heavy. → 2b: Parameter-Efficient Finetuning (PEFT) - Only a small subset of parameters is added and updated (e.g., via LoRA, Adapters, or IA³). - Base model remains frozen. - Much cheaper, ideal for rapid iteration and deployment. - Multi-LoRA architectures (e.g., used in Fireworks AI, Hugging Face PEFT) allow hosting multiple finetuned adapters on the same base model, drastically reducing cost and latency for serving. Step 3: Alignment (Usually via RLHF) Pretrained and task-tuned models can still produce unsafe or incoherent outputs. Alignment ensures they follow human intent. Alignment via RLHF (Reinforcement Learning from Human Feedback) involves: → Step 1: Supervised Fine-Tuning (SFT) - Human labelers craft ideal responses to prompts. - Model is fine-tuned on this dataset to mimic helpful behavior. - Limitation: Costly and not scalable alone. → Step 2: Reward Modeling (RM) - Humans rank multiple model outputs per prompt. - A reward model is trained to predict human preferences. - This provides a scalable, learnable signal of what “good” looks like. → Step 3: Reinforcement Learning (e.g., PPO, DPO) - The LLM is trained using the reward model’s feedback. - Algorithms like Proximal Policy Optimization (PPO) or newer Direct Preference Optimization (DPO) are used to iteratively improve model behavior. - DPO is gaining popularity over PPO for being simpler and more stable without needing sampled trajectories. Key Takeaways: → Pretraining = general knowledge (expensive) → Finetuning = domain or task adaptation (customize cheaply via PEFT) → Alignment = make it safe, helpful, and human-aligned (still labor-intensive but improving) Save the visual reference, and follow me (Aishwarya Srinivasan) for more no-fluff AI insights ❤️ PS: Visual inspiration: Sebastian Raschka, PhD

LLMs aren’t just pattern matchers... they learn on the fly. A new research paper from Google Research sheds light on something many of us observe daily when deploying LLMs: models adapt to new tasks using just the prompt, with no retraining. But what’s happening under the hood? The paper shows that large language models simulate a kind of internal, temporary fine-tuning at inference time. The structure of the transformer, specifically the attention + MLP layers, allows the model to "absorb" context from the prompt and adjust its internal behavior as if it had learned. This isn’t just prompting as retrieval. It’s prompting as implicit learning. Why this matters for enterprise AI, with real examples: ⚡ Public Sector (Citizen Services): Instead of retraining a chatbot for every agency, embed 3–5 case-specific examples in the prompt (e.g. school transfers, public works complaints). The same LLM now adapts per citizen's need, instantly. ⚡ Telecom & Energy: Copilots for field engineers can suggest resolutions based on prior examples embedded in the prompt; no model updates, just context-aware responses. ⚡ Financial Services: Advisors using LLMs for client summaries can embed three recent interactions in the prompt. Each response is now hyper-personalized, without touching the model weights. ⚡ Manufacturing & R&D: Instead of retraining on every new machine log or test result format, use the prompt to "teach" the model the pattern. The model adapts on the fly. Why is this paper more than “prompting 101”? We already knew prompting works. But we didn’t know why so well. This paper, "Learning without training: The implicit dynamics of in-context learning" (Dherin et al., 2025), gives us that why. It mathematically proves that prompting a model with examples performs rank-1 implicit updates to the MLP layer, mimicking gradient descent. And it does this without retraining or changing any parameters. Prior research showed this only for toy models. This paper shows it’s true for realistic transformer architectures, the kind we actually use in production. The strategic takeaway: This strengthens the case for LLMs in enterprise environments. It shows that: * Prompting isn't fragile — it's a valid mechanism for task adaptation. * You don’t need to fine-tune models for every new use case. * With the right orchestration and context injection, a single foundation model can power dozens of dynamic, domain-specific tasks. LLMs are not static tools. They’re dynamic, runtime-adaptive systems, and that’s a major reason they’re here to stay. 📎 Link to the paper: http://bit.ly/4mbdE0L

AI’s Umwelt and the Conditions of Meaning: Interpretation, Cognition, and Alien Epistemology The “stochastic parrot” critique, introduced by Emily Bender, Timnit Gebru, and colleagues in 2021, has become a dominant framework for denying that large language models possess cognitive capacities. On this view, systems such as GPT generate plausible language by reproducing statistical patterns in training data, without understanding or meaning. Meaning is said to arise only when a human interprets the output. The system itself remains inert with respect to sense. N. Katherine Hayles challenges this conclusion by revising the conditions under which meaning is said to occur. She defines cognition as a process that interprets information in contexts connected to meaning, a formulation that explicitly decouples cognition from consciousness, intentionality, and symbolic reference. Once interpretation, not subjectivity, becomes the criterion, the question shifts from whether AI understands like humans to how interpretation operates within nonhuman systems. Hayles draws on the concept of umwelt to describe these bounded horizons of interpretation. An umwelt designates the domain within which information can register as relevant and actionable for a given system. It does not describe an experiential interior or semantic autonomy. Rather, meaning arises within an umwelt as a function of interpretive responsiveness under material limits. LLMs instantiate such limits materially. Textual input is segmented into tokens and transformed into vectors positioned within a high-dimensional space learned through training. This space does not encode meanings symbolically. It encodes relations of proximity, difference, and contextual salience shaped by patterned co-occurrence across vast corpora. Attention mechanisms modulate these relations dynamically, weighting which vectors matter in a given context. Text generation proceeds through the selection of subsequent tokens based on these weighted relations rather than through retrieval of stored meanings or execution of rules. Within Hayles’s framework, these operations count as interpretation. The system continuously selects among alternatives relative to contextual conditions internal to its architecture and training history. These selections are neither random nor imposed externally at the moment of reading. They emerge within the system’s operational horizon. Meaning, in this sense, is generated within the AI’s umwelt rather than conferred retroactively by human interpretation.  This does not imply that AI meanings resemble human meanings or that they are accessible in the same way. They are umwelt-relative, shaped by material architecture and inferential constraint. Hayles’s point is not to elevate machine language to human status, but to reject the assumption that meaning must mirror human semantics to count at all. This marks an alien epistemology grounded in constraint rather than human semantics.

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!

How do large language models like GPT, Claude, or LLaMA actually work? It’s less “mystical AI” and more clever math layered at scale. > Tokens as numbers: Text is broken into tokens, each mapped to a vector (a list of numbers) that encodes meaning. > Position matters: Because models don’t naturally know order, positional signals are added so “dog bites man” isn’t confused with “man bites dog.” > Attention: Every token looks at every other token to decide what’s relevant. Multi-head attention lets the model track grammar, tone, and meaning in parallel. > Feedforward layers: After attention, tokens pass through small neural networks that refine their representations. > Residuals & normalization: Inputs and outputs are combined and smoothed, preventing the model from drifting off course. > Stacking layers: Dozens of these blocks are layered. Early layers capture word meanings, middle layers handle relationships, and later layers build context. > Output: Finally, the model converts vectors into probabilities and selects the most likely next token - repeating until the answer is complete. What feels like fluent conversation is really billions of calculations stacked to uncover patterns in language. Not magic - just math at scale. #innovation #technology #future #management #startups