How Large Language Models Create Text Responses

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

Happy Friday, this week in #learnwithmz lets explore the inner workings of Large Language Models via 𝐋𝐋𝐌 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧! I recently came across an incredible visualization of a GPT-based large language model https://bbycroft.net/llm by Brendan Bycroft (https://lnkd.in/g5cxifcZ). Let's do walkthrough of the mechanics of a nano-GPT model with 85,000 parameters, showcasing how it processes sequences of tokens to predict the next in line. 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 - Token Processing: The model takes a sequence of tokens and sorts them in alphabetical order. - Embedding: Each token is transformed into a 48-element vector. - Transformer Layers: The embedding passes through multiple transformer layers, refining predictions at each step. - Output Prediction: The model predicts the next token in the sequence with impressive accuracy. 𝐋𝐋𝐌 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 Here are brief explanations for each component of large language models (LLMs): - Embeddings: Transform input tokens into dense vectors that capture semantic meaning. - LayerNorm: Normalizes the inputs across the features to stabilize and accelerate training. - Self Attention: Allows the model to weigh the importance of different tokens in a sequence for better context understanding. - Projection: Maps the high-dimensional vectors to a different space, often reducing dimensionality. - MLP (Multi-Layer Perceptron): A feedforward neural network that processes the transformed data for complex pattern recognition. - Softmax: Converts the model’s outputs into probabilities, highlighting the most likely predictions. - Output: The final prediction or generated token based on the processed and weighted inputs. This visualization is a fantastic resource for anyone looking to understand the fundamentals of how large language models work. Check it out and dive into the fascinating world of AI with LLMs! #AI #MachineLearning #DeepLearning #LLM #GPT #DataScience

Quick AI Lesson: 𝗟𝗮𝗿𝗴𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗹𝗶𝗸𝗲 𝗖𝗵𝗮𝘁𝗚𝗣𝗧, 𝗚𝗲𝗺𝗶𝗻𝗶 𝗮𝗻𝗱 𝗖𝗹𝗮𝘂𝗱𝗲 𝗱𝗼𝗻'𝘁 “𝗿𝗲𝗮𝗱” 𝘁𝗵𝗲 𝘄𝗮𝘆 𝘄𝗲 𝗱𝗼; 𝘁𝗵𝗲𝘆 𝗴𝘂𝗲𝘀𝘀 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝘄𝗼𝗿𝗱. For LLMs to guess well, it needs two skills: 𝗦𝘆𝗻𝘁𝗮𝘅 = the structure of a sentence. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰𝘀  = the meaning of words in context. 𝗜𝗺𝗮𝗴𝗲 1: A diagram illustrating how structure and meaning help pick the right words. 𝗜𝗺𝗮𝗴𝗲 2: “bank” = a money place; “interest” = the money you earn or pay. 𝗜𝗺𝗮𝗴𝗲 3: “bank” = the side of a river; “interest” = curiosity. Same words. Different meanings. LLMs use these words based on how they are trained. 𝗛𝗼𝘄 𝗹𝗮𝗿𝗴𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀) 𝗮𝗿𝗲 𝘁𝗿𝗮𝗶𝗻𝗲𝗱: 1️⃣ Break text into pieces (tokens): Words and parts of words become small chunks. "the", "bank", "near", "the", "river"... 2️⃣ Practice guessing the next token: The model reads huge amounts of text and tries to predict the next chunk. When it’s wrong, it learns from the mistake. 3️⃣ What it learns while guessing: ▪️It notices grammar patterns so it can track who is doing what (syntax-like skills). ▪️It learns that the same word can mean different things depending on the sentence (semantics). 𝘕𝘰𝘵𝘦: 𝘛𝘩𝘦 𝘮𝘰𝘥𝘦𝘭 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘳𝘶𝘯 𝘢 𝘣𝘶𝘪𝘭𝘵-𝘪𝘯 𝘨𝘳𝘢𝘮𝘮𝘢𝘳 𝘱𝘢𝘳𝘴𝘦𝘳 𝘣𝘺 𝘥𝘦𝘧𝘢𝘶𝘭𝘵; 𝘪𝘵 𝘫𝘶𝘴𝘵 𝘭𝘦𝘢𝘳𝘯𝘴 𝘱𝘢𝘵𝘵𝘦𝘳𝘯𝘴 𝘵𝘩𝘢𝘵 𝘰𝘧𝘵𝘦𝘯 𝘭𝘪𝘯𝘦 𝘶𝘱 𝘸𝘪𝘵𝘩 𝘨𝘳𝘢𝘮𝘮𝘢𝘳. 4️⃣ Instruction tuning: Later, the model is shown examples of good question→answer pairs so it follows directions better. 5️⃣ Preference tuning: Humans give feedback about which answers people prefer so it responds in more helpful ways. 6️⃣ Getting facts right: For up-to-date or specific info, you add retrieval augmented generation (RAG) so the model looks things up instead of guessing. 𝗥𝗲𝘃𝗶𝗲𝘄: LLMs learn by guessing the next word. To guess well, they learn syntax (sentence structure) and semantics (word meaning). That’s why the model can tell the difference between a river bank and a money bank.

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