Pratica di Shadowing: Most devs don't understand how LLM tokens work - Impara a parlare inglese con i video
Creazione lezione...
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So many devs are working with LLMs these days, and they don't really know about the fundamentals.
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I was teaching an AI workshop recently in Poland, and I asked people to put up their hands if they knew what a token was.
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And I don't know whether it's my AI bubble on X or something, but I was pretty shocked because only about a third of the people in the workshop put up their hand.
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And so I wanted to put together a token deep dive for those of you who are using LLMs, but kind of need to catch up on what's going on under the hood.
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And of course, we're going to explain all of these
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I promise I will never show a line of Python on this channel, that is for sure.
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Let's start with a diagram here.
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Tokens are the currency of LLMs.
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When you send an input of "Hello World" to an LLM, that gets broken down into its constituent tokens.
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If you send "Hello World" to OpenAI, that is three tokens billed at an infinitesimally small amount per 1k tokens.
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This is for GPT -5, I think.
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Then the LLM will do some thinking and produce some output tokens. to the input tokens.
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So to get the total amount you spent, you take that number, divide it by a thousand and times it by a cent.
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You do the same with the input tokens, you add them together and you've got your total cost for that API call.
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I'm going to demonstrate that using the ASDK.
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For those who are not familiar with the ASDK, I've got a great free tutorial on my site AIHero, which I'll link up there.
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Now we're going to generate some text using Claude 3 .5 Haiku, a nice cheap model from Anthropic.
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And we're going to send it "Hello World".
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We're then going to log things.
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We're going to log out the text that it produced and also the usage.
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This usage, which we get from the AASDK, is the amount of tokens that we used.
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We can see that it replied with "Hello, how are you doing today?
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Is there anything I can help you with?" which used 20 output tokens, but bizarrely 11 input tokens.
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That's pretty weird considering we only prompted it with like two words: "Hello world".
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Let's do the same thing again, but this time we're going to call Google.
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We'll use Gemini 2 .0 flashlight and we'll log out 11 and 20,
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but in the Google version we only used 4 input tokens and 11 output tokens.
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So this feels a bit like a mystery if you don't know how tokens work.
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What is the relationship between tokens and text?
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And why does the same prompt sent to two different models end up with a different number of tokens?
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Okay, let's reveal the mystery and talk about what tokens actually are.
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Every LLM has a different token vocabulary.
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And these tokens are all subwords gets assigned a number and the number is the token.
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When we call an LLM it actually encodes the text that we send it into tokens.
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So hello world gets split up into its largest individual tokens in the vocabulary.
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In this case that might mean hello space world and exclamation mark
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and then it identifies the numbers in the vocabulary that match that word or subword.
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We can actually take a look at this in code in TypeScript because OpenAI's token.
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We're using O200KBASE, which is the tokenizer for the model GPT -40.
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I've also got here some text at input .md.
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"The wise owl of moonlight forest, where ancient trees stretch their branches toward the starry sky." We're reading that big chunk of text into memory here,
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and we're using the GPT -40 tokenizer to encode a bunch of tokens, which are just an array of numbers.
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Then we'll compare the input length in characters and the number is nearly 2300 characters,
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but the number of tokens is less than 500.
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And we end up with just a bunch of numbers here.
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If we go ahead and test this with something simpler, like Hello World, then here the content length is 12 and the number of tokens is only 3.
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These tokens then are what gets fed into the LLM while it does its processing.
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Let's look at a diagram of the full LLM process end -to -end.
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We pass in Hello World, it gets split into the largest chunks that it knows about in the
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by just looking up the number assigned to them in the token vocabulary, then the LLM does some thinking here and it outputs some more tokens.
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In other words, all the computation here is being done on numbers, not on text.
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And these output tokens then get decoded back into text, joined together and you get the final output.
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So that's the process.
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And while you think that the LLM is dealing with text, it's actually dealing with these numeric representations of chunks of text.
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we can decode these tokens here using tokenizer .decode.
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Decode is just a function that takes in an array of numbers, tokens, and returns a single concatenated string.
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And when we run this, that is exactly what we get.
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And by the way, if you're digging this, then I have something cooking for you on aihero .dev.
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And I've got a newsletter where I post breakdowns like this, which will be in the description below.
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So we know what tokens are now, but we don't really know why they differ between model providers.
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To understand this, we need to know how these vocabularies even get built and why different model providers do them differently.
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Here's the process for how tokenizers get trained.
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Let's imagine you've got a corpus of text here.
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We're going to use an extremely small corpus, just the single line, the cat sat on the mat.
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But in reality, this would be gigabytes, terabytes of data.
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The same data, in fact, usually that the model itself is trained on.
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Now, we can build an extremely simple tokenizer just from this one sentence.
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We can take this piece of text and extract out the unique characters in it.
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I've created a character level tokenizer that takes in a dataset and returns a tokenizer.
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And yes, it's in a class.
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TypeScript classes are good, folks.
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I've been saying this for a while.
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We'll get to this implementation in a minute, but let's actually look at the usage.
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We have our dataset of the cat sat on the mat.
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Again, in a real application this might be gigabytes of text.
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Then we grab our tokenizer by instantiating a character level tokenizer, and I'm passing in an input of cat sat mat.
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We're then encoding the input, turning it into tokens, and logging out some information about the tokens.
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When we run this we can see that the input length, the number of characters in catSatMAT is 11.
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Then the number of tokens that we produce at the end is also 11.
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This is because we only have characters as tokens.
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If we remove all these logs and just log something called tokenizer .mapping we can see our entire vocabulary of tokens.
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And we can see there are only 10 tokens in the entire vocabulary.
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Space is 3, n is 8, o is 7.
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So that means always going to be the same as the number of characters.
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And this is not good because the more tokens we have in the input, the more work it's going to be for our LLM to process them.
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The vocabulary size is really, really important.
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Let's say we only have about a thousand tokens in our vocabulary.
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If we take in the word understanding, it might produce five tokens: un, de, st, and ing.
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If we bump that up to 50 ,000,
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then we might get larger 200k we might split it into just two tokens.
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And two tokens is a lot more efficient for the LLM to process than five tokens.
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So these are the trade -offs that different models and different model providers make.
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By the way, you can't just scale this to infinity too, because the larger your vocabulary, the bigger the model needs to be in order to house that vocabulary.
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And of course, the more memory it takes to execute.
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So then we know that our implementation of character level of tokens.
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So how do we get from character level to adding some more subwords?
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Well, we identify different groups that commonly occur together.
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For instance, th occurs in the and the.
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He occurs in the and the as well.
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And then at occurs in cat, sat and mat.
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I've got an example of this called the subword level tokenizer.
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Now, I've basically vibe coded this, so don't look too closely at the implementation.
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But let's take a peek at the usage down below.
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We have our same data set and our same
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And let's take a look at how many tokens we get back from the input of cat, sat, and mat.
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We see here that the input cat, sat, mat is 11 and we get 8 tokens back.
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We have achieved this by calculating some subwords in the dataset.
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Let's look at this by logging out tokenizer .mapping.
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And we can see here that we now have 15 tokens.
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At, the, and her are all in as our e space and t space have each been assigned their tokens.
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This means our encode function is a little more complicated possible subword so that it can tokenize the input text efficiently.
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The implementation is below if you want to take a closer look.
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In our implementation, we only go one level deep.
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And so we end up with groupings that are only two characters long.
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But real tokenizers identify larger and larger groups by identifying groups of groups.
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So for instance, in this example, th and he often appear together.
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And so a group of three tokens, the, makes sense.
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Finally, then we end up with a set of numbers
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that are each assigned to the individual token token as we saw in our implementation.
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Finally, let's look at how a tokenizer behaves when it encounters an unusual word.
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This is O Frabjous Day from the Lewis Carroll poem.
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Frabjous is just a word that Lewis Carroll made up, so it's not going to be very frequent in the dataset.
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When we run this through O200K, Frabjous is actually split up into four separate tokens here.
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And so unusual words are split up into more tokens than words that occur more frequently in the dataset.
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This also means that if you're querying the LLM in a language that's not present in the dataset much, it's likely to break it up into more tokens because it's not used to those combinations of letters.
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This is true for spoken languages, but also for coding languages.
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And this is yet another advantage that more commonly used coding languages have in the AI era.
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It's fewer tokens to send 20 lines of JavaScript than it is to send 20 lines of Haskell or something.
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So let's sum up here.
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Tokens are the currency of LLMs, and you are charged by the token.
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But different model providers will treat these inputs differently and produce different numbers of tokens.
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text into tokens.
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You take a piece of text, you split it into the largest tokens you can find, and then you turn them into the numbers that you have them stored in the vocabulary.
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Decoding is much simpler even, you just take the numbers, you find the relevant chunks in your vocabulary, and you join them together.
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And the whole LLM process involves an encoding step, then the LLM thinks, produces some output tokens, which then get decoded into text.
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So tokens are really what the LLM does its thinking in.
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If you dug this video, then I have plenty more content like this coming.
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I've been full -time thinking about AI plus TypeScript for a year now.
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And if you're interested in learning more about AI or building with AI, especially, then this channel is for you.
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I personally think that TypeScript is the language for building AI -powered applications, whereas Python is probably the thing you want to use if you're building models.
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hero .dev if you want to follow along.
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I have something dropping there very, very soon.
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If you like this explanation, then what should I cover next?
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Please comment below.
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Thanks for following along, and I will see you very soon.
📺 Stesso Canale
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Vocabolario e note di pronuncia per questa lezione
Questo video contiene 145 frasi e 2089 parole da ripetere con lo shadowing. Il parlato dura 10:57. Chi parla va veloce, circa 191 parole al minuto: aspettati suoni legati e ridotti. Solo il 80% delle parole rientra nelle 3.000 più comuni dell’inglese, quindi il lessico è impegnativo.
Vocaboli chiave di questo video
15 parole meno comuni del video, con pronuncia e significato:
- token /ˈtoʊkən/ (sostantivo) — simbolo, segno. Something serving as an expression of something else.
- input /ˈɪnpʊt/ (sostantivo) — immissione. The act or process of putting in; infusion.
- vocabulary /vəˈkæb.jə.lə.ɹi/ (sostantivo) — vocabolario. A usually alphabetized and explained collection of words e.g. of a particular field, or prepared for a specific purpose, often for learning.
- output /ˈaʊtpʊt/ (sostantivo) — risultato, produzione. Production; quantity produced, created, or completed.
- implementation /ˌɪmplɪmənˈteɪʃən/ (sostantivo) — implementazione, attuazione. The process of moving an idea from concept to reality. In business, engineering and other fields, implementation refers to the building process rather than the…
- occur /əˈkɜː/ (verbo) — verificarsi. To happen or take place.
- usage /ˈjuːsɪd͡ʒ/ (sostantivo) — utilizzo, utilizzi. A custom or established practice.
- provider /pɹəˈvaɪ.də(ɹ)/ (sostantivo) — fornitore. One who, or that which, provides a service, commodity, or the means for subsistence.
- assign /əˈsaɪn/ (verbo) — assegnare, accantonare. To designate or set apart (something) for some purpose.
- chunk /t͡ʃʌŋk/ (sostantivo) — pezzo, tocco. A part of something that has been separated; a generally squat, thick, irregular piece of something, e.g. wood or stone.
- log /lɔɡ/ (sostantivo) — tronco. The trunk of a dead tree, cleared of branches.
- mystery /ˈmɪs.t(ə.)ɹi/ (sostantivo) — mistero, arcano. Something secret or unexplainable; an unknown.
- unusual /ʌnˈjuːʒ(u)əl/ (aggettivo) — insolito, particolare. Not usual, out of the ordinary.
- commonly /ˈkɑmənli/ (avverbio) — comunemente, frequentemente. As a rule; frequently; usually.
- currency /ˈkʌɹ.ən.si/ (sostantivo) — valuta. Money or other items used to facilitate transactions.
I phrasal verb che sentirai
- look at (verbo) — guardare. To observe or watch.
- take in /teɪk‿ˈɪn/ (verbo) — impegnare, vincolare. To receive.
- deal with (verbo) — fare i conti. To handle verbally or in some form of artistic expression; to address or discuss as a subject.
- put up (verbo) — collocare. To place in a high location.
- sit on (verbo) — bloccare, sospendere. To block, suppress, restrain (something).
- go ahead /ˌɡoʊ əˈhɛd/ (verbo) — procedere, andare avanti. To proceed with; to begin.
- go on /ˈɡoʊˌɒn/ (verbo) — continuare. To continue in extent.
- make up /ˌmeɪk ˈʌp/ (verbo) — comporsi. To constitute, to compose.
Pronuncia a cui fare attenzione
Chi parla usa 38 contrazioni e forme ridotte, come we're, I've, don't. Pronunciale nella forma breve, così come le senti.
- I suoni “sh” e “zh”: implementation /ˌɪmplɪmənˈteɪʃən/, unusual /ʌnˈjuːʒ(u)əl/, workshop /ˈwɝk.ʃɑp/, shock /ʃɔk/, explanation /ˌɛkspləˈneɪʃən/
- Parole lunghe — attenzione all’accento: vocabulary /vəˈkæb.jə.lə.ɹi/, implementation /ˌɪmplɪmənˈteɪʃən/, unusual /ʌnˈjuːʒ(u)əl/, differently /ˈdɪf.ə.ɹənt.li/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
- Registrati e confronta con l’originale, facendo attenzione a parole come token, input, vocabulary.
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | being done · is trained · been assigned |
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | I've created · have achieved · I've been |
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | those who are · tokens, which are · this, which will |
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.