Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 12 — Real-World Applications of LLMs
Twelfth post of the LLM Primer II walkthrough. Text generation, summarization, QA, translation, reasoning — and the constrained decoding, agent loops, and multimodal generalization that turn one next-token machine into a dozen kinds of product.
2026-03-14
Chapter 10 — Post-Training and Alignment Mathematics
Tenth post of the LLM Primer II walkthrough. The mathematics that civilizes a brilliant but feral next-word predictor into a helpful assistant — supervised fine-tuning, reward modeling, RLHF on a KL leash, and the elegant DPO derivation that collapses the whole pipeline into a single supervised loss.
2026-03-12
Chapter 7 — Efficiency and Transformer Variants
Seventh post of the LLM Primer II walkthrough. The computational complexity of attention, the GPU memory and throughput math that constrains real systems, FlashAttention derived from first principles, and the family of clever variants — multi-query, gated, low-rank — that keep big models running.
2026-03-09
Chapter 4 — Attention: The Core Mechanism
Fourth post of the LLM Primer II walkthrough. Self-attention derived from intuition, the geometry of queries/keys/values, multi-head structure and normalization, softmax in detail with its temperature knob, and a striking final move: attention seen as a kernel method.
2026-03-06
Chapter 3 — Mathematical Tools for Language Models
Third post of the LLM Primer II walkthrough. The probability and statistics you actually need for language modeling, the slice of linear algebra that matters, and embeddings as the first place those two tools meet inside an LLM.
2026-03-05
Chapter 10 — Safety, Ethics, & Trust: Beyond the Marketing
Chapter 10 of the LLM Primer I series. The honest picture of LLM safety — why hallucinations happen mechanistically, where bias actually lives, how layered guardrails work, and why governance is the institutional layer that technical controls can't replace. For practitioners who need to ship safely.
2026-02-27
Chapter 7 — Beyond Next-Token Prediction: Embeddings, Retrieval, and Multimodality
Chapter 7 of the LLM Primer I series. The capabilities that turn a next-token predictor into something much more — embeddings, semantic search, retrieval-augmented generation, and the move into multimodal inputs. How RAG actually keeps an LLM grounded in real documents instead of confabulating.
2026-02-24
Chapter 4 — The Transformer Architecture: Inside the Engine of Modern AI
Chapter 4 of the LLM Primer I series. A tour of the Transformer block — how self-attention, positional encoding, and stacked layers combine to produce the architecture every modern LLM is built on. Includes a clear explanation of why scaling Transformers works, and what it costs.
2026-02-212.1 What Is a Large Language Model?
A clear and in-depth explanation of what Large Language Models (LLMs) are. Learn how LLMs map token sequences to probability distributions, why next-token prediction unlocks general intelligence, and what makes a model “large.” This section builds the foundation for understanding pretraining, parameters, and scaling laws.
2025-09-08
7.0 Future Outlook and Challenges
A preview from Chapter 7: Explore the future of large language models—ethics, efficiency, multimodal AI, and responsible governance beyond scaling.
2024-10-06
4.4 How LLMs Write Code: The Rise of AI-Powered Programming Assistants
Explore how large language models (LLMs) generate and complete code from natural-language prompts, and what it means for the future of software development.
2024-09-27
4.2 Enhancing Customer Support with LLM-Based Question Answering Systems
Discover how Question Answering Systems powered by Large Language Models (LLMs) are transforming customer support, search engines, and specialized fields with high accuracy and flexibility.
2024-09-17
4.0 Applications of LLMs: Text Generation, Question Answering, Translation, and Code Generation
Discover how Large Language Models (LLMs) are used across various NLP tasks, including text generation, question answering, translation, and code generation. Learn about their practical applications and benefits.
2024-09-15
3.2 LLM Training Steps: Forward Propagation, Backward Propagation, and Optimization
Explore the key steps in training Large Language Models (LLMs), including initialization, forward propagation, loss calculation, backward propagation, and hyperparameter tuning. Learn how these processes help optimize model performance.
2024-09-13
1.2 The Role of Large Language Models (LLMs) in Natural Language Processing (NLP)
Discover the impact of Large Language Models (LLMs) on natural language processing tasks. Learn how LLMs excel in text generation, question answering, translation, summarization, and even code generation.
2024-09-04