Monthly AI Guides

Each monthly guide turns one month of HowAIModelsWork.com articles into a connected plain-English learning route. Use this page when you want the big picture for a whole month.

January 2026

AI model basics

Models, tokens, hallucinations, training data, context windows, fine-tuning, alignment, guardrails, reasoning, and evaluation.

Open January guide →
February 2026

RAG, agents, vision, and generative AI

RAG, embeddings, vector databases, function calling, AI agents, machine learning, vision models, image generation, and AI music.

Open February guide →
March 2026

Transformers, attention, and prompts

Tokens, transformers, attention, positional encoding, activation functions, prompts, retrieval, grounding, sampling, temperature, and context behavior.

Open March guide →
April 2026

AI video, memory, speed, and cost

AI video generation, long scenes, memory pressure, KV cache, quantization, mixture of experts, speed, cost, and state.

Open April guide →
May 2026

AI assistants, code, files, and review

AI assistants, uploaded files, code assistants, long code files, summaries, customer support, search reliability, workflows, and human review.

Open May guide →
June 2026

Reasoning, agents, training data, local AI, and voice

Reasoning models, chain-of-thought prompting, AI agents, multi-agent mistakes, training data, synthetic data, model collapse, on-device AI, and speech-to-text.

Open June guide →
July 2026

Voice AI in progress

July is currently building around AI voice: how AI generates human-sounding voice, why voice assistants pause, and why voices can sound real without being human.

Readers Also Read

Why AI Gives Different Answers to the Same Prompt

You ask the exact same question twice. One answer begins with an analogy; the other gives a definition—and both may be reasonable. AI does not retrieve one fixed response. It chooses from several likely next pieces of text, and a tiny early choice can redirect everything that follows. When is that variation useful, and when does it reveal uncertainty?
Read more

What Is a Context Window? Why AI Forgets Earlier Parts of a Conversation

The AI remembers your rule at the start of a conversation, then later repeats itself, changes direction, or acts as if the rule was never there. It is not choosing to forget. The earlier text may have slipped outside the model’s active view. What exactly is a context window, and why does it make long conversations less stable?
Read more

Why AI Sometimes Repeats Itself

The answer begins clearly, then quietly starts saying the same thing again with different words. It keeps getting longer, but the amount of useful information barely grows. AI repetition is often a prediction loop, not a deliberate choice. Why do familiar phrases become easier to continue—and how can one safe pattern trap the rest of an answer?
Read more

Why AI Gives Different Answers to the Same Question

You ask the same question twice and receive two polished answers that do not quite match. The wording may change harmlessly—or the underlying claim may shift too. That happens because AI generates each response token by token instead of retrieving one fixed reply. How can small early choices grow into a completely different answer by the end?
Read more

Where Did AI Get Its Training Data?

An AI model can write about science, history, code, and everyday life—but its training material didn’t come from one neat digital library. Public web pages, licensed collections, human feedback, specialist datasets, and synthetic examples may all play a role. The harder question is what happens before that material is trusted enough to shape the model.
Read more