Most technically literate people dont struggle with AI because its too hard. They struggle because the vocabulary feels like a private club handshake. Conversations move fast. Words like token, embedding, context window, logits, and temperature get tossed around casually as if everyone in the room silently agreed on their meaning years ago.
The truth is simpler and far less intimidating.
AI discussions often compress complex ideas into short labels. Those labels sound mathematical, abstract, or even mystical. But underneath each term is a very practical concept. When someone says increase the temperature or we hit the context limit, they are not invoking sorcery they are describing predictable mechanical behaviors of a system that processes numbers at scale.
Large Language Models dont understand language the way humans do. They operate on structured patterns of numbers. To participate confidently in AI conversations, you dont need to code. You dont need to understand backpropagation. You dont need to derive equations. You just need to decode the vocabulary.
This section is about that decoding.
Well start with the foundational layer the mechanics of how language becomes numbers, how numbers become probabilities, and how probabilities become words again. Once you understand this layer, the rest of modern AI RAG systems, agents, fine-tuning stops feeling magical and starts feeling architectural.
1. Token
Core Idea: A token is the smallest chunk of text a model processes not necessarily a whole word.
A token is not the same as a word. It is a chunk of text that a language model processes as a single unit. Sometimes a token is a full word (apple). Sometimes its part of a word (un + break + able). Sometimes its punctuation or even a space.
Think of tokens as Lego pieces of language. Sometimes you need several small pieces to build one word, and sometimes one piece is enough similar to how some blocks are bigger than others.
Large Language Models do not read sentences the way humans do. They convert text into tokens, and then convert those tokens into numbers. From that point onward, the model is not dealing with language it is dealing with sequences of integers. When someone says this prompt used 800 tokens, they are describing how much numerical input the model had to process.
Understanding tokens is important because cost, speed, and memory limits in AI systems are usually measured in tokens not words.
2. Context Window
Core Idea: The context window is a models short-term working memory the maximum number of tokens it can handle at once.
Now that we know how language is split into digestible pieces, lets talk about how many of those pieces a model can hold at once.
The context window is the maximum number of tokens a model can handle at once. Think of it as the models short-term working memory.
Every prompt you send, plus every response the model generates, consumes tokens inside this window. Once the total exceeds the limit, older tokens get dropped. The model doesnt remember them anymore not because it forgot conceptually, but because they literally no longer exist in its active input.
Imagine the model as a chef preparing a dish. The context window is the counter space where ingredients are laid out. Once the counter is full, older ingredients must be moved to the pantry (or thrown out) to make room for new ones.
This is why long conversations sometimes drift. If critical instructions were given early and the context window overflows, the model may stop following them. It isnt being careless. It simply cannot see that earlier text anymore.
3. Tokenization
Core Idea: Tokenization is the process of breaking raw text into tokens and mapping each token to a numeric ID.
Before the model can work with language numerically, it needs to convert text into numbers first.
Tokenization is the process of breaking raw text into tokens and mapping each token to a numeric ID.
When you type a sentence, it first passes through a tokenizer. The tokenizer decides how to split the text into chunks based on patterns learned during training. Common algorithms like Byte Pair Encoding (BPE) are used to efficiently break text into reusable fragments.
Think of the tokenizer as a language translator who converts English into a secret numerical code. Each word or syllable gets assigned a unique number, and this code is the only thing the model can read.
Once tokenized, each piece is replaced with a number from a large vocabulary. From that moment forward, the model processes only numbers. Language becomes math. And that transformation is the foundation of everything that follows.
4. Text Decoding
Core Idea: Text decoding is the strategy a model uses to select the next token from a probability distribution.
Now that we understand how language gets converted into numbers, heres where it gets interesting: how does the model decide which word comes next?
After a model processes your input, it predicts a probability distribution for the next possible token. It doesnt choose a sentence. It predicts probabilities for what token should come next.
Text decoding is the strategy used to select one token from that probability distribution. Once selected, the token is appended to the sequence, and the process repeats one token at a time until the response is complete.
Imagine the model as a autocompleting mind that considers every possible next word, assigns a confidence score to each, and then picks one to continue the thought. The decoding strategy determines how adventurous or cautious that selection process is.
Different decoding strategies produce different styles of output. Some are deterministic and predictable. Others introduce randomness for creativity. The decoding method significantly influences how the final answer feels.
5. Temperature
Core Idea: Temperature controls how much randomness is allowed during token selection how predictable or creative the output becomes.
If decoding decides which word to pick, temperature decides how much the model is allowed to gamble.
Temperature controls how much randomness is allowed during token selection.
At low temperature (close to 0), the model strongly favors the most probable next token. Responses become predictable, focused, and consistent. This is useful for factual tasks or structured output.
At higher temperatures, less probable tokens are given more opportunity to be selected. The output becomes more creative, varied, and sometimes surprising. Push it too high, and the text can become chaotic. Temperature doesnt make a model smarter it changes how adventurous it is when choosing words.
Low temperature is like a formal dinner where everyone follows proper etiquette. High temperature is like a lively party where unexpected things might happen and thats part of the fun.
6. Top-K Sampling
Core Idea: Top-K sampling limits the models choices to only the K most probable next tokens.
Sometimes even with temperature, the model might consider thousands of options. Top-K narrows the field.
Top-K sampling limits the models choices to the K most probable next tokens.
If K is 10, the model ignores all tokens except the 10 highest-probability candidates. It then selects one from those. This prevents extremely unlikely words from appearing while still allowing some variation.
Imagine voting for a national election versus voting for class president. Top-K is like saying Only the top 10 candidates can win the class presidency this keeps things manageable but still democratic.
Its a way of balancing safety and creativity. You constrain the decision space without making the output fully deterministic.
7. Top-P (Nucleus Sampling)
Core Idea: Top-P sampling selects from the smallest set of tokens whose combined probability exceeds a threshold P making it adaptive to the models confidence.
Top-K uses a fixed number. But what if some questions have obvious answers and others are genuinely uncertain? Enter Top-P.
Top-P sampling takes a different approach. Instead of choosing a fixed number of tokens, it selects from the smallest set of tokens whose combined probability exceeds a threshold P (for example, 0.9).
If a few tokens already account for 90% of the probability mass, the candidate pool is small. If the distribution is flatter, the pool becomes larger. This makes Top-P adaptive to the shape of the probability curve.
Top-P is like saying Focus on the top candidates until you hit 90% confidence. If only one candidate has 95% confidence, look at just one. If no one stands out, look at more options until you feel sure.
In practice, Top-P often produces more natural text than Top-K because it adjusts dynamically to how confident the model is at each step.
8. Logits
Core Idea: Logits are the raw scores a model produces before converting them into probabilities.
Before probabilities come logits the raw, unprocessed confidence scores.
Logits are the raw scores a model produces before converting them into probabilities.
When the model evaluates possible next tokens, it assigns each one a numerical score. These scores are not yet probabilities they are unnormalized values. A mathematical function (usually softmax) converts logits into probabilities that sum to 1.
Think of logits as rough draft confidence scores, and probabilities as the final polished percentages that add up to 100%.
When people talk about adjusting logits or logit bias, they are referring to modifying those raw scores before the probability step. Its a way of nudging the model toward or away from specific tokens.
9. Hallucination
Core Idea: Hallucination is when a model generates plausible-sounding but factually incorrect information.
Now that we understand how the model generates text, heres the important caveat: sometimes it generates fiction that sounds like fact.
Hallucination happens when a model generates information that sounds plausible but is factually incorrect.
This occurs because language models are prediction machines, not verification engines. They generate text based on patterns learned during training. If a question requires precise, up-to-date, or rare information, the model may produce something that statistically fits even if it is wrong.
Hallucination is like a confident storyteller who has never visited the places they describe but has read so many travelogues that they can narrate one perfectly convincingly. Theyre not lying they just never learned the difference between imagination and reality.
Hallucination is not deception. It is a byproduct of probabilistic generation. Systems like Retrieval-Augmented Generation (RAG) are designed to reduce hallucinations by grounding responses in external data sources.
Your Mental Model: The AI Chef
Imagine the model as a chef in a kitchen:
- Tokens are the ingredients on the counter
- Context Window is the counter space available
- Tokenization is how the ingredients are prepped and measured
- Logits are the chefs initial instincts about which ingredient to use next
- Temperature, Top-K, Top-P are the cooking style: precise and consistent versus experimental and creative
- Text Decoding is the actual decision to pick an ingredient and add it to the dish
- Hallucination is when the chef confidently uses an ingredient that actually went bad
At this point, AI stops feeling mystical. It becomes a system of structured numerical decisions, like a very sophisticated recipe-following machine.
What You Now Know
Models:
- Convert text into tokens (Lego pieces of language)
- Operate within a memory window (counter space)
- Predict probabilities (assign confidence scores)
- Select tokens using controlled randomness (cooking style)
Next, we move from language mechanics to meaning itself embeddings and vectors where text stops being sequence and becomes geometry.