What are Large Language Models (LLMs)? LLMs Explained with a Pizza Analogy — Transcript

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1. 0:01AI is everywhere, but what's actually
2. 0:04happening inside a tool like chat GPT?
3. 0:06Forget complex code and vector
4. 0:09databases. Today, we're going to explain
5. 0:11how a large language model works by
6. 0:14building the perfect AI pizza. Exactly.
7. 0:17That's exactly what you heard. To
8. 0:19understand an LLM, you first have to
9. 0:21understand what it ate.
10. 0:25Okay. So, let's let's start with the
11. 0:27ingredients, the training data.
12. 0:29The LLM's training data is basically its
13. 0:32ingredient library. Imagine this library
14. 0:35contains every book, every article,
15. 0:38every Reddit read, and every
16. 0:40conversation ever published online. The
17. 0:44LLM consumes all of it, learning every
18. 0:47possible word, every sentence structure,
19. 0:50and every single fact.
20. 0:53But the LLM doesn't just learn the
21. 0:55ingredients. It also learns the
22. 0:57relationships between them. If you see
23. 1:00the words pepperoni, mozzarella, and
24. 1:02crust, you know it's a pizza. The LLM
25. 1:05learns those same statistical
26. 1:07connections. So the capacity to store
27. 1:10all those millions of statistical
28. 1:12connections is what we call parameters.
29. 1:14The bigger the parameters, the larger
30. 1:17the kitchen, the smarter the model. GPT4
31. 1:21has so many parameters. It's like having
32. 1:23a huge industrial kitchen that can
33. 1:25memorize a billion recipes.
34. 1:29All right. Now,
35. 1:31second part to the story, the chef's
36. 1:33goal. Token prediction. The fundamental
37. 1:37purpose of any LLM is simply token
38. 1:39prediction. Token is the unit of
39. 1:42language. It can be a word like apple or
40. 1:45just a piece of a word like in. When you
41. 1:49type a prompt, the LLM starts a chain
42. 1:51reaction. It looks at your prompt,
43. 1:54calculate the most statistically likely
44. 1:57next token, generates it, then looks at
45. 2:00that new result, and calculates the next
46. 2:02token again, and so on. Think of it like
47. 2:06a statistical chef. If you say, "I want
48. 2:09a pizza with tomato, cheese, and the
49. 2:13chef doesn't think about what tastes
50. 2:15goods. He just knows that out of the
51. 2:17millions of orders he's seen, pepperoni
52. 2:20is the most frequent word to follow. So
53. 2:23the LLM is basically a calculator, not a
54. 2:26thinker.
55. 2:28There is one limitation, the context
56. 2:30window. This is the chef's short-term
57. 2:33memory. He only remembers the last few
58. 2:36sentences in your order. So if you give
59. 2:38him a huge 50step prompt, he might
60. 2:41forget the first three ingredients you
61. 2:43asked for by the time he gets to the
62. 2:45end.
63. 2:47All right, the third point here, the
64. 2:50human touch. Fine-tuning.
65. 2:52If the raw LLM is just a data crunching
66. 2:55calculator, why does it talk so nicely?
67. 2:58Because of fine-tuning.
68. 3:01After the initial massive training, the
69. 3:03model is guided by humans who rank its
70. 3:06output for quality, tone, and safety.
71. 3:09This is called reinforcement learning
72. 3:11with human feedback or RLHF.
73. 3:15This is how we take the raw data chef
74. 3:17and turn him into a polite assistant who
75. 3:20doesn't generate harmful or incorrect
76. 3:23information.
77. 3:24Finally, your conversation starts with a
78. 3:27prompt, the order ticket. Your prompt
79. 3:30tells the chef the style. Make a pizza,
80. 3:33long text, using only Italian
81. 3:36ingredients. That's the tone. And make
82. 3:38it spicy. That's the style. The quality
83. 3:41of your order determines the quality of
84. 3:43the final pizza. So, the quality of your
85. 3:45product is really important.
86. 3:49All right. Summing it up,
87. 3:53an LLM is not a brain. It is a powerful,
88. 3:57highly trained statistical chef who's
89. 3:59memorized the entire world's menu. It
90. 4:03does not create new recipes. It just
91. 4:06predicts the next ingredient in the
92. 4:08perfect sequence based on all of the
93. 4:10data it has consumed.
94. 4:13Um, so does that make sense to you? Is
95. 4:16there any other complex AI topics that
96. 4:19you'd like me to explain with an
97. 4:21analogy? Drop it in the comments below.
98. 4:24And if the pizza and the chef analogy
99. 4:26clicked for you, hit the like and
100. 4:28subscribe subscribe button for more AI
101. 4:31explained simply.