LLM architecture evolved from RNNs that read one word at a time, to Attention, to the Transformer, to scaling, and to Mixture of Experts (MoE) that we use today.

Evolution of LLM Architecture

Top-k Sampling keeps a fixed number of the most likely tokens, and Top-p Sampling keeps the top tokens whose total probability reaches p. The LLM picks from them.

How do Top-k and Top-p Sampling work?

Jev is a System One Model, a new kind of AI model that does not write text. It only makes fast decisions, with a confidence score, that our software can use directly.

Jev and System One Models Explained

A Real-Time Voice AI Agent listens to us, understands, thinks, takes actions, and talks back in a natural voice within a fraction of a second. Let's design one.

Design a Real-Time Voice AI Agent

Temperature is a single number that controls how an LLM picks the next token. Low Temperature gives safe, predictable answers, and high gives creative ones.

How does Temperature control LLM output?

Recursive Self-Improvement (RSI) is when an AI system improves its own abilities, and then the improved version improves itself further, again and again.

What is Recursive Self-Improvement (RSI)?

N-gram Speculation makes an LLM write faster by guessing the next few tokens from matching text it has already seen, and then verifying them in one run.

What is N-gram Speculation in LLMs and How Does It Speed Up Generation?

Prefill-Decode Disaggregation runs an LLM so that reading the prompt (prefill) and writing the answer (decode) happen on separate machines, each tuned for its job.

What is Prefill-Decode Disaggregation in LLM Inference?

KV Cache Compression is a set of techniques that shrink the memory an LLM uses to remember the conversation, using quantization, token eviction, and more.

What is KV Cache Compression?

Chunking for RAG means cutting a big document into smaller pieces so that the AI can find the right piece at the right time. We will learn the best strategies.

How to Chunk Documents for RAG? Chunking Strategies Explained

Vectorless RAG answers questions from our documents without embeddings or a vector database. The LLM reads the document structure and decides which part to open.

What is Vectorless RAG? RAG Without Embeddings or a Vector Database

Attention sinks are the first few tokens that get a huge share of attention in an LLM. Keeping them lets StreamingLLM handle very long conversations without breaking.

How do Attention Sinks work?

In Sliding Window Attention, each word looks only at a fixed number of nearby words instead of all the words, which makes attention fast for long text.

How does Sliding Window Attention work?

TensorRT-LLM is an open-source library from NVIDIA that makes LLMs run as fast as possible on NVIDIA GPUs by preparing the model ahead of time.

How does TensorRT-LLM work?

An LPU (Language Processing Unit) is a chip built for one job, running a trained LLM and producing text as fast as possible, by keeping the model on the chip.

How does an LPU work?

EAGLE is a speculative decoding method that makes an LLM generate text faster, without changing its output, by guessing future tokens at the feature level.

What is EAGLE? Feature-Level Speculative Decoding Explained

Medusa makes an LLM generate text 2 to 3 times faster by adding extra heads that guess several future tokens at once, and then checking them together.

What is Medusa? Multi-Head Speculative Decoding Explained

An RNN reads a sentence one word at a time, carrying a memory, whereas a Transformer reads all the words at once and uses attention to connect them directly.

How do RNNs and Transformers differ?

AI does its best work when we can clearly check if a task is done, and it struggles when only a human can judge it. That check is our definition of done.

AI Is Only as Good as Our Definition of Done

Prefix Tuning keeps a large language model frozen and trains only a small set of extra numbers, called the prefix, to adapt the model to a new task cheaply.

How does Prefix Tuning work?

Graph Engineering is designing an AI system as a graph, where every step is a node and every path between steps is an edge, instead of one giant prompt.

What is Graph Engineering?

Loop Engineering is designing the repeating cycle that an AI agent runs, so that it keeps making real progress on a task and stops at the right moment.

What is Loop Engineering?

The Lost in the Middle problem is when an LLM uses the beginning and the end of a long input well, but pays very less attention to what is in the middle.

What is the Lost in the Middle Problem in LLMs and How to Fix It?

LLM watermarking hides a signal inside the text a model writes. A secret key slightly nudges the word choices, and a detector with the same key finds it later.

How Does LLM Watermarking Work?

Deep RL from Human Preferences is the 2017 paper that taught machines what we want by asking humans to pick the better of two clips. It is the origin of RLHF.

What is Deep RL from Human Preferences? The Paper That Started RLHF

InstructGPT is the model that taught GPT-3 to follow our instructions by learning from human feedback (RLHF). This work led directly to ChatGPT.

What is InstructGPT? How GPT-3 Learned to Follow Instructions

ColBERT is a retrieval method that keeps the word-by-word matching of a slow BERT reranker but makes it fast enough to search millions of passages.

What is ColBERT? Late Interaction Retrieval Explained

LLM guardrails are safety checks that sit around an LLM to control what goes in and what comes out, and they stop dangerous or unwanted answers.

How do LLM guardrails work?

In Cloud Deployment, the AI model runs on a server and the device asks it over the internet. In On-device Deployment, the model runs inside the device itself.

Cloud vs On-device Model Deployment

In Transformers, the Encoder reads the whole input in both directions to understand it, whereas the Decoder generates the output one token at a time.

Encoder vs Decoder in Transformers

Generative AI is a type of AI that can create new things, like text, images, audio, video, and code. We will learn how it learns and creates something new.

What is Generative AI?

Prompt Injection is an attack where someone slips their own instructions into the text sent to an LLM, so that it follows the attacker instead of the developer.

What is Prompt Injection in LLMs and How Do We Defend Against It?

Precision tells us how many of the items we flagged were actually correct, whereas Recall tells us how many of all the real items we actually caught.

Precision vs Recall

An embedding is a list of numbers that represents the meaning of something, so that things with similar meaning get similar numbers. It powers AI search.

What are Embeddings?

An Agent Skill is a folder of instructions, and optionally scripts, that an AI agent loads by itself only when the task needs it. Let's see how Skills work.

What are Agent Skills?

MCP (Model Context Protocol) is an open standard that defines one common way for AI applications to connect to outside tools and data. It was created by Anthropic.

What is MCP (Model Context Protocol)?

Fine-tuning is taking a model that is already trained and training it a little more on our own data, so that it becomes good at our specific task.

How does fine-tuning work?

OKF (Open Knowledge Format) is an open standard for writing what an organization knows about its data as plain markdown files that any AI agent can read.

What is OKF (Open Knowledge Format)?

Semantic Search finds results based on meaning, not exact words. It turns text into embeddings and returns the items whose meaning is closest to the query.

How does Semantic Search work?

Cursor is an AI code editor. It indexes our codebase, understands it by meaning, and helps us with Tab autocomplete, Chat, and Agent mode to write code faster.

How does Cursor work?

Context compaction shrinks the old part of a long conversation into a short summary, so that the important facts stay and the context window gets free space again.

How does context compaction work?

Claude Code is a coding agent from Anthropic that runs in the terminal. It completes tasks by reading our code, editing files, running commands, and checking its work.

How does Claude Code work?

llama.cpp runs LLMs on everyday hardware by shrinking the model with quantization, loading it fast with memory mapping, and sharing work between CPU and GPU.

How does llama.cpp run LLMs on everyday hardware?

Model Quantization stores and computes a model's numbers at lower precision, so the model takes less memory and runs faster, even on a laptop or a phone.

How does Model Quantization work?

Chain-of-Thought (CoT) Prompting is a technique where we ask the model to write out its reasoning steps before the final answer, which makes it more accurate.

How does Chain-of-Thought (CoT) Prompting work?

Prompt Chaining breaks one big task into smaller prompts, where the output of one prompt becomes the input of the next. It solves bigger tasks reliably.

How does Prompt Chaining work?

PyTorch is an open-source library to build and train machine learning models. It uses tensors, a computation graph, and autograd, and runs fast on the GPU.

How does PyTorch work?

Semantic Caching is a cache that matches questions by their meaning instead of their exact words, so an AI app can reuse past answers for similar questions.

How does Semantic Caching work?

Hybrid Search combines keyword search and semantic search, and merges their results into one final ranked list, so that we get the best of both.

How does Hybrid Search work?

HyDE (Hypothetical Document Embeddings) is a RAG technique where the AI first writes a fake answer to the question, and then we search using that fake answer.

How does HyDE work in RAG?

Prefill reads the whole prompt in one pass and produces the first token, whereas Decode generates the rest of the tokens one at a time using the KV cache.

Prefill vs Decode: LLM Inference Optimization

A GPU is a chip that does a huge number of simple calculations at the same time. Deep learning is mostly matrix multiplication, so GPUs make it very fast.

How does a GPU work for Deep Learning?

LangGraph is a framework to build LLM applications where the work is organized as a graph of steps, with nodes, edges, and a shared state.

How does LangGraph work?

LangChain is a framework that helps us build LLM applications by connecting the LLM with our data, tools, and logic through chains, memory, and agents.

How does LangChain work?

SGLang is a high-performance framework that serves LLMs to many users at the same time, as fast as possible, using ideas like RadixAttention to reuse past work.

How does SGLang work?

Approximate Nearest Neighbor (ANN) Search finds items that are very close to the most similar one, much faster, by allowing a tiny chance of not being exact.

How does Approximate Nearest Neighbor (ANN) search work?

A TPU (Tensor Processing Unit) is a chip made by Google for machine learning. It uses a systolic array to do matrix multiplication very fast and efficiently.

How does a Google TPU work?

An image embedding is a list of numbers that represents the meaning of an image. Similar images get similar numbers, so we can compare and search images easily.

How do Image Embeddings work?

A computer-use agent is an AI program that operates a computer like a human. It looks at the screen, moves the mouse, clicks, and types to finish our goal.

How do Computer-Use Agents work?

Sakana Fugu is a family of AI models that work like a conductor. They decide which other AI models should work on our question and combine their answers.

What is Sakana Fugu? The Technical Report Explained

A Diffusion Language Model writes text by starting from pure gibberish and cleaning it up again and again, instead of writing one word at a time from left to right.

How do Diffusion Language Models (DLMs) work?

An Embedding Cache stores the embeddings we have already computed, so that we reuse them for the same text instead of computing them again. It saves money and time.

How does an Embedding Cache work?

A World Model is an AI that learns an internal copy of how an environment behaves, so that it can predict what happens next when an action is taken.

How do World Models work?

vLLM is a high-throughput engine for serving LLMs. It uses PagedAttention to manage the KV cache memory efficiently, so it can serve many more users at once.

How does vLLM work?

LLM Inference Optimization is the set of techniques, like KV Cache, Flash Attention, and Continuous Batching, that make LLMs fast and scalable in production.

LLM Inference Optimization

Function Calling lets an LLM use external tools and APIs. The model does not run the function itself. It tells our code which function to call and with what inputs.

How does Function Calling work in LLMs?

GGUF is a single file format that stores everything needed to run an LLM locally, like the weights, the tokenizer, and the settings, all in one file.

How does GGUF work?

Knowledge Distillation is a technique where we train a small model to copy the behavior of a big model, so that we can run powerful AI at a low cost.

How does Knowledge Distillation work?

Token Streaming sends the LLM's reply piece by piece as each piece is produced, using SSE over one open connection, instead of waiting for the whole reply.

How does Token Streaming work?

Prompt Caching saves the work an LLM already did for a repeated part of a prompt, so that it can reuse that work next time. It makes responses faster and cheaper.

How does Prompt Caching work?

A Reranker is a model that takes a list of documents and reorders them, putting the most relevant ones at the top for a given question. It makes RAG more accurate.

How does a Reranker work?

JEPA (Joint Embedding Predictive Architecture) is an idea from Yann LeCun where a model predicts the hidden part of the data in the embedding space, not in pixels.

What is Joint Embedding Predictive Architecture (JEPA) and How Does It Work?

A Vector Database stores data as lists of numbers and helps us find items that are similar in meaning, not just items that match exactly. It powers AI search.

How does a Vector Database work?

Dropout randomly switches off some neurons in a neural network during training, so that the network does not depend too much on any single neuron.

What is Dropout in Neural Networks and How Does It Work?

A Generative Adversarial Network (GAN) is a system where two neural networks compete, and through this, one learns to create new data that looks real.

What are Generative Adversarial Networks (GANs) and How Do They Work?

A Diffusion Model creates new images by starting from pure random noise and cleaning it up step by step. This is how DALL-E and Stable Diffusion work.

What are Diffusion Models and How Do They Generate Images?

A Variational Autoencoder (VAE) is an Autoencoder that learns a smooth, organized latent space, so that we can pick any point from it to generate new data.

What are Variational Autoencoders (VAEs) and How Do They Work?

Continual Learning is the ability of an LLM to keep learning new information over time, without forgetting what it already learned. We will see how it works.

What is Continual Learning in LLMs? Solving Catastrophic Forgetting

Multi-Head Attention runs many Self Attention operations in parallel, each focusing on a different aspect of the sentence, and combines their outputs.

What is Multi-Head Attention in Transformers and How Does It Work?

Cross Attention is a mechanism where one sequence looks at a different sequence, using its own Queries against the Keys and Values of the other sequence.

What is Cross Attention in Transformers and How Does It Work?

Self Attention lets every word in a sentence look at every other word in the same sentence to understand its meaning. It is the heart of models like BERT and GPT.

What is Self Attention in Transformers and How Does It Work?

The AI Agent Loop is the code that runs an AI Agent. It asks the LLM what to do, runs that action, feeds back the result, and repeats until the task is done.

What is an AI Agent Loop?

AI Agent Observability means recording every step an AI Agent takes, like each thought and tool call, so that we can see why it behaved the way it did.

What is AI Agent Observability? Traces, Spans, and Metrics Explained

AI agents communicate by sending messages to each other to share information, ask for help, and work together. We will learn the main ways and protocols.

How AI Agents Communicate

An AI SubAgent is a smaller, specialized agent that works under a main agent to handle one part of a big task, like a team member under a manager.

What are AI SubAgents? Why We Need Them and How They Work

LLM Evaluation is the process of measuring how well a Large Language Model performs on the tasks we expect it to do, using metrics, benchmarks, and more.

What is LLM Evaluation? Metrics, Benchmarks, and Methods Explained

AI Agent Evaluation is the process of measuring how well an AI Agent performs a task, by checking the final result, the steps it took, and the tools it used.

How to Evaluate AI Agents? Metrics, Methods, and Best Practices

AI Orchestration is the process of coordinating many AI parts, like LLMs, tools, data sources, and agents, so that they work together to finish a complex task.

What is AI Orchestration? How It Works and the Common Patterns

LLM as a Judge is a technique where we use one LLM to evaluate the output of another LLM against our criteria, and give a score or a verdict with a reason.

What is LLM as a Judge? How to Use an LLM to Evaluate LLM Outputs

Contrastive Learning teaches a model by comparing things. It pulls similar things close together and pushes different things far apart. Let's see how it works.

What is Contrastive Learning? How It Works Step by Step

A Recursive Language Model (RLM) handles very large inputs by letting the model call another language model, or itself, on smaller parts of the input.

What are Recursive Language Models (RLMs) and How Do They Work?

GRPO is a reinforcement learning algorithm to train LLMs. It generates a group of answers for the same question and teaches the model to prefer the better ones.

What is Group Relative Policy Optimization (GRPO) and How Does It Work?

Direct Preference Optimization (DPO) trains an LLM directly on human preference data, without a separate reward model and without reinforcement learning.

What is Direct Preference Optimization (DPO) and How Does It Work?

Proximal Policy Optimization (PPO) is a reinforcement learning algorithm that improves the policy in small, safe steps. It is used to train LLMs with RLHF.

What is Proximal Policy Optimization (PPO) and How Does It Work?

Batch Normalization normalizes each feature across all examples in a batch, while Layer Normalization normalizes all features within each single example.

Batch Normalization vs Layer Normalization

RLHF (Reinforcement Learning from Human Feedback) teaches an LLM to give the responses that humans prefer, by turning human preferences into a reward signal.

What is RLHF? Reinforcement Learning from Human Feedback Explained

An Autoregressive Model generates one piece at a time, like one token, by predicting the next step from everything it has generated so far. GPT works this way.

What are Autoregressive Models?

A Large Reasoning Model (LRM) is an LLM that thinks step by step before it answers, producing a long chain of reasoning first. This makes it better at hard problems.

What are Large Reasoning Models (LRMs) and How Are They Different from LLMs?

Continuous Batching lets LLM servers handle many more users by replacing each finished request with a new one right away, so that the GPU is never idle.

What is Continuous Batching in LLMs and How Does It Work?

Small Language Models (SLMs) are language models with typically less than 10 billion parameters. They are cheaper and faster, and can run on our own devices.

What are Small Language Models (SLMs) and When Should We Use Them?

Android NDK lets us use C and C++ code in our app, while RenderScript runs heavy computations at high performance. Both aim to improve app performance.

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Android NDK and RenderScript

ThreadPoolExecutor in Android

Bitmap Pool in Android