शैडोइंग अभ्यास: Most devs don't understand how LLM tokens work - वीडियो के साथ अंग्रेजी बोलना सीखें
पाठ बनाया जा रहा है...
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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.
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इस पाठ की शब्दावली और बोलने के सुझाव
इस वीडियो में शैडोइंग के लिए 145 वाक्य और 2089 शब्द हैं। बोला गया हिस्सा 10:57 लंबा है। वक्ता तेज़ बोलता है, लगभग 191 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। केवल 80% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।
इस वीडियो की मुख्य शब्दावली
वीडियो के 15 कम प्रचलित शब्द, उच्चारण और अर्थ के साथ:
- vocabulary /vəˈkæb.jə.lə.ɹi/ (संज्ञा) — शब्दावली. A usually alphabetized and explained collection of words e.g. of a particular field, or prepared for a specific purpose, often for learning.
- implementation /ˌɪmplɪmənˈteɪʃən/ (संज्ञा) — कार्यान्वयन. 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/ (क्रिया) — बीतना. To happen or take place.
- usage /ˈjuːsɪd͡ʒ/ (संज्ञा) — लौकिकता. A custom or established practice.
- provider /pɹəˈvaɪ.də(ɹ)/ (संज्ञा) — प्रदाता, दाता. One who, or that which, provides a service, commodity, or the means for subsistence.
- assign /əˈsaɪn/ (क्रिया) — निर्दिष्ट, आवंटित करना. To designate or set apart (something) for some purpose.
- log /lɔɡ/ (संज्ञा) — कुंदा, लट्ठा. The trunk of a dead tree, cleared of branches.
- mystery /ˈmɪs.t(ə.)ɹi/ (संज्ञा) — रहस्य, राज़. Something secret or unexplainable; an unknown.
- commonly /ˈkɑmənli/ (क्रिया विशेषण) — सामान्यतः. As a rule; frequently; usually.
- currency /ˈkʌɹ.ən.si/ (संज्ञा) — मुद्रा, करंसी. Money or other items used to facilitate transactions.
- workshop /ˈwɝk.ʃɑp/ (संज्ञा) — कर्मशाला, कारख़ाना. A room, especially one which is not particularly large, used for manufacturing or other light industrial work.
- diagram /ˈdaɪ.ə.ɡɹæm/ (संज्ञा) — आलेख, मापचित्र. A plan, drawing, sketch or outline to show the function or operation of something, or to show the relationships between the parts of a whole.
- complicated /ˈkɑm.plɪˌkeɪ.tɪd/ (विशेषण) — जटिल, पेचीदा. Difficult or convoluted.
- shock /ʃɔk/ (संज्ञा) — झटका. A sudden, heavy impact.
- compare /kəmˈpɛɚ/ (क्रिया) — तौलना. To assess the similarities and differences between two or more things [“to compare X with Y”]. Having made the comparison of X with Y, one might have found it…
वीडियो में आने वाली फ़्रेज़ल वर्ब
- look at (क्रिया) — हेरना. To observe or watch.
- go ahead /ˌɡoʊ əˈhɛd/ (क्रिया) — आगे बढ़ना. To proceed with; to begin.
- think about /ˈθɪŋk əˌbaʊt/ (क्रिया) — चिंतन करना. To ponder.
ध्यान देने योग्य उच्चारण
वक्ता 38 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे we're, I've, don't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “sh” और “zh” की ध्वनियाँ: implementation /ˌɪmplɪmənˈteɪʃən/, unusual /ʌnˈjuːʒ(u)əl/, workshop /ˈwɝk.ʃɑp/, shock /ʃɔk/, explanation /ˌɛkspləˈneɪʃən/
- लंबे शब्द — बलाघात सही रखें: vocabulary /vəˈkæb.jə.lə.ɹi/, implementation /ˌɪmplɪmənˈteɪʃən/, unusual /ʌnˈjuːʒ(u)əl/, differently /ˈdɪf.ə.ɹənt.li/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/
इस वीडियो से अभ्यास कैसे करें
- पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
- 0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
- अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर vocabulary, implementation, occur जैसे शब्दों पर ध्यान दें।
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | being done · is trained · been assigned |
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | I've created · have achieved · I've been |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | those who are · tokens, which are · this, which will |
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।