Artificial Intelligence

Artificial Intelligence / 03 Sep 2026 / 7 min read

Tokenization in Large Language Models

Large language models do not read text as words or characters in the way people do. Before text reaches the model, a tokenizer converts it into a sequence of discrete units called tokens and maps those tokens to numerical identifiers. Tokenization is easy to overlook because most model APIs perform it automatically. Yet token boundaries affect context-window usage, inference cost, truncation, multilingual behavior, and even whether two visually similar strings are represented in similar ways. These details explain many LLM behaviors that otherwise look inconsistent.

Artificial Intelligence LLM

Artificial Intelligence / 03 Sep 2026 / 10 min read

Tokenization in Language Models

Language models do not read text as a sequence of words. Before a model can process a prompt, a tokenizer converts the text into a sequence of token IDs from a fixed vocabulary. The model operates on those IDs, and generated IDs are later converted back into text. This extra layer is easy to ignore because most model APIs accept ordinary strings. But tokenization affects how much text fits in a context window, how usage-based costs are calculated, how text is truncated or split, and why seemingly small formatting changes can alter model behavior.

Artificial Intelligence Language Models

Artificial Intelligence / 03 Sep 2026 / 9 min read

Stop Model Training at the Right Time with Early Stopping

Training a model for more epochs does not guarantee a better model. Training loss may keep falling while performance on unseen data stops improving or begins to degrade. Continuing from that point consumes compute and can leave you with a checkpoint that generalizes worse than an earlier one. Early stopping turns validation performance into a stopping rule. Instead of choosing a fixed number of epochs and hoping it is appropriate, you monitor a validation metric, keep the best checkpoint, and stop after the metric has failed to improve for a defined amount of time.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 10 min read

Stabilize Neural Network Training with Gradient Clipping

Neural network training can look healthy for many steps and then suddenly become unstable. The loss may jump, parameters may receive an unusually large update, or numerical values may become non-finite. One possible cause is an exploding gradient: the gradient becomes large enough that the resulting optimization step is destructive. Gradient clipping puts a limit on gradients before the optimizer uses them. It is especially useful when occasional gradient spikes are expected, but it is not a general repair for a bad learning rate, broken data, or an incorrect training loop.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 10 min read

Speculative Decoding for Faster LLM Inference

Autoregressive language models generate text sequentially. After processing the prompt, the model predicts a next token, appends that token to the sequence, and repeats the process. That dependency makes generation difficult to parallelize across time: token 101 cannot normally be generated until token 100 is known. Speculative decoding changes the amount of useful work performed during each expensive target-model step. A cheaper draft process proposes several future tokens, then the target model verifies those proposals together. When enough proposals are accepted, the application can advance by multiple tokens while invoking the large model fewer times.

Artificial Intelligence LLM

Artificial Intelligence / 03 Sep 2026 / 6 min read

Self-Attention in Transformer Models

Transformers can process relationships between tokens without stepping through a sequence one token at a time. The mechanism that makes this possible is self-attention: each token builds a weighted view of other tokens in the same context. The formula is compact. The sections below show what the calculation does, how masking sets the information boundary, and where the computational cost comes from. Start with token representations Before attention runs, each input token is represented by a vector. Let the matrix X contain those token representations. A transformer layer applies learned projections to produce three matrices:

Artificial Intelligence Transformer

Artificial Intelligence / 03 Sep 2026 / 7 min read

Reduce LLM Hallucinations with Grounding and Verification

Large language models can produce fluent answers that contain incorrect facts, invented details, or unsupported claims. This behavior is commonly called hallucination. Hallucinations are not simply random mistakes. A language model generates tokens that are plausible given its input and learned parameters. Plausible text is not necessarily true text, especially when the model lacks reliable evidence for the question being asked. For developers, the practical goal is therefore not to find a single setting that eliminates hallucinations. It is to design the application so that factual claims are grounded in appropriate evidence, uncertainty is handled explicitly, and important outputs are verified.

Artificial Intelligence LLM

Artificial Intelligence / 03 Sep 2026 / 10 min read

Positional Information in Transformer Models

Self-attention can compare every token with other tokens in a context, but the comparison alone does not tell the model where those tokens occur. A sentence is not just a collection of words: changing their order can change the meaning. Transformer models therefore need a way to represent positional information. This mechanism lets the network distinguish, for example, the first occurrence of a token from a later occurrence and reason about relationships such as “the previous token” or “far earlier in the document.”

Artificial Intelligence Transformer

Artificial Intelligence / 03 Sep 2026 / 9 min read

Mixture-of-Experts Models

A neural network does not have to use every parameter for every input. A mixture-of-experts (MoE) layer takes advantage of this idea by keeping several expert networks and using a router to select only a small subset for each token. This separates total parameter count from the number of parameters active for one token. That can increase model capacity without making the arithmetic performed for every token grow in direct proportion to the total number of expert parameters.

Artificial Intelligence Neural Networks

Artificial Intelligence / 03 Sep 2026 / 10 min read

LoRA for Parameter-Efficient Fine-Tuning

Fine-tuning a large model does not always require updating every model parameter. Low-Rank Adaptation (LoRA) takes advantage of this idea by keeping the original model weights frozen and learning much smaller matrices that modify selected layers. For developers, LoRA changes what must be trained, stored, and moved between experiments—not just the size of the fine-tuning job. That distinction determines when it is useful, what it does not save, and how adapter choices affect model behavior.

Artificial Intelligence LLM

Artificial Intelligence / 03 Sep 2026 / 7 min read

LLM Quantization for Efficient Inference

Large language models can require substantial memory bandwidth and compute during inference. Quantization reduces those requirements by representing some model values with fewer bits than the floating-point formats commonly used during training. The idea sounds simple: store numbers with lower precision. In practice, quantization trades memory, latency, hardware support, implementation complexity, and model quality. Choose the configuration from measurements rather than assuming that fewer bits are always better. What quantization changes A neural network contains many numerical values, especially weights. A model stored with 16-bit weights needs roughly two bytes per weight before accounting for runtime buffers and other overhead. If those weights can instead be represented with 8 or 4 bits, their raw storage requirement falls substantially.

Artificial Intelligence LLM

Artificial Intelligence / 03 Sep 2026 / 11 min read

Learning Rate Warmup and Decay for Stable Training

A neural network can have the right architecture, clean training data, and a sensible optimizer yet still train poorly because its learning rate changes at the wrong pace. The learning rate controls the scale of parameter updates. A rate that is too large can make optimization unstable or skip useful regions of the loss landscape. A rate that is too small can make progress unnecessarily slow. The appropriate rate can also change during training: cautious updates may help at the beginning, larger updates can drive progress once training is stable, and smaller updates can help refine the model later.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 9 min read

Label Smoothing in Classification Models

A classification model is often trained as if the correct class deserves all of the target probability and every other class deserves none. For a three-class problem, an example labeled cat might therefore use this target: cat: 1.00 dog: 0.00 fox: 0.00 That target is convenient, but it asks the model to push probability toward an extreme even when labels are imperfect, classes overlap, or the input is genuinely ambiguous. Label smoothing changes the training target so that a small amount of probability mass is assigned away from the labeled class.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 7 min read

KV Caching in LLM Inference

Large language models generate text one token at a time. Without an optimization, every new token would force the model to repeat attention calculations for tokens it has already processed. KV caching avoids much of that repeated work. During inference, the model stores the key and value representations produced by attention layers for previous tokens. When generating the next token, it can reuse those stored representations instead of recomputing them from the beginning.

Artificial Intelligence LLM

Artificial Intelligence / 03 Sep 2026 / 9 min read

Knowledge Distillation for Smaller AI Models

A large model may produce useful predictions but still be too expensive or slow for the environment where it must run. A mobile application, an edge device, or a high-volume service can have tighter limits on memory, latency, and compute. Knowledge distillation is one way to address that gap. Instead of training a smaller model only from the original labels, we also train it to imitate information produced by a stronger teacher model. The smaller model is called the student.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 7 min read

Improve RAG Retrieval with Reranking

Retrieval-augmented generation (RAG) depends on finding useful evidence before asking a language model to answer. A vector search can retrieve candidates quickly, but the nearest vectors are not always the passages that best answer the user’s question. Reranking adds a second relevance step. The system first retrieves a reasonably broad candidate set with a fast method, then applies a more precise model to reorder those candidates before selecting context for the LLM.

Artificial Intelligence RAG

Artificial Intelligence / 03 Sep 2026 / 10 min read

Design Reliable LLM Tool Calls with Structured Outputs

Giving a language model access to tools changes what an AI application can do. Instead of only producing text, the model can request a database lookup, search a document index, calculate a value, or trigger an application operation. The difficult part is not exposing a function. It is deciding which responsibilities belong to the model and which must remain under application control. A useful mental model is: model proposes an action application validates the proposal application decides whether to execute it tool returns data model explains or uses the result This separation makes tool-calling systems easier to reason about. The model handles interpretation and selection. Deterministic application code handles authorization, validation, execution, and state changes.

Artificial Intelligence LLM

Artificial Intelligence / 03 Sep 2026 / 8 min read

Choose Classification Thresholds with Precision and Recall

A binary classifier often produces a score rather than a final yes-or-no answer. An image model might estimate a 0.82 probability that a component is defective, while a moderation model might assign a 0.37 score to unwanted content. The classification threshold turns that continuous score into a decision. A threshold of 0.5 is common, but it is not automatically correct. The right threshold depends on which mistakes matter, how frequently the positive class occurs, and what happens after the model makes a prediction.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 6 min read

Choose Between Prompting, RAG, and Fine-Tuning

When an AI application produces weak results, teams often jump directly to fine-tuning. That can be the right choice, but many problems are cheaper and easier to solve with better prompting or retrieval-augmented generation (RAG). The three approaches change different parts of the system. Prompting changes the instructions and context given at inference time. RAG supplies relevant external information at inference time. Fine-tuning changes the model’s learned parameters through additional training.

Artificial Intelligence LLM

Artificial Intelligence / 03 Sep 2026 / 10 min read

Calibrate Classifier Confidence for Better Decisions

A classifier can predict the correct label often and still produce confidence scores that are difficult to trust. Suppose a model marks 1,000 transactions as fraudulent with confidence near 0.9. If that confidence behaves like a useful probability, roughly 90% of comparable predictions should actually be fraud. If only 65% are, the model is overconfident. If nearly all are fraud, it is underconfident. This distinction matters whenever a system uses model scores to make decisions: escalating cases to humans, approving automated actions, ranking alerts, or choosing a threshold based on expected risk. Accuracy tells you how often predictions are correct. Calibration asks whether predicted probabilities match observed frequencies.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 9 min read

Beam Search for Sequence Generation

A model that generates text or another sequence makes a series of local decisions. At each step, it assigns scores or probabilities to possible next tokens. The simplest decoder chooses the most likely token, appends it, and repeats. That strategy is called greedy decoding. It is cheap and easy to understand, but an early choice that looks best by itself can lead to a worse complete sequence. Once greedy decoding commits to that choice, it cannot reconsider it.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 9 min read

Batch LLM Inference for Better Throughput

An LLM server can receive many requests at the same time, yet processing every request independently is often an inefficient way to use an accelerator. GPUs and similar hardware are designed to perform large amounts of parallel numerical work. A single small request may leave part of that capacity unused. Batching combines work from multiple requests so the model can process more of it together. This can improve total throughput, but it introduces an important trade-off: waiting to form a batch can delay individual requests, and requests with different sequence lengths do not all consume the same amount of work.

Artificial Intelligence LLM

Artificial Intelligence / 03 Sep 2026 / 8 min read

Activation Functions in Transformer Feed-Forward Networks

Attention gets much of the attention in transformer explanations, but every transformer layer also contains a feed-forward network that performs substantial computation on each token representation. The activation function inside that network is a small-looking design choice with an important job: it introduces nonlinearity so the network can learn transformations that stacked linear projections alone cannot express. The feed-forward block matters when reading model architectures, comparing implementations, estimating parameter and compute costs, or deciding whether two designs are actually equivalent.

Artificial Intelligence Transformer

Artificial Intelligence / 02 Sep 2026 / 5 min read

Semantic Caching for LLM Applications Without Serving Stale Answers

Large language model requests are expensive compared with ordinary cache lookups. When users repeatedly ask questions with slightly different wording, an exact string cache misses even though the intended answer may be identical. A semantic cache uses vector similarity to decide whether a new request is close enough to a previous request that its answer can be reused. The idea is attractive, but the difficult part is not storing embeddings. It is deciding when reuse is actually safe.

Artificial Intelligence LLM