Shadowing Practice: Prompt engineering - Introduction - Learn English Speaking with Video
Creando lección...
1
Welcome to this course on ChatGPT problem engineering for developers.
2
I'm thrilled to have with me Iza Fulford to teach this along with me.
3
She is a member of the technical staff of OpenAI and had built the popular ChatGPT retrieval plugin.
4
And a large part of work has been teaching people how to use OEM or large language model technology in products.
5
She's also contributed to the OpenAI cookbook that teaches people prompting.
6
So thrilled to have you with you.
7
And I'm thrilled to be here and share some prompting best practices with you all.
8
I'm not sure.
9
So there's been a lot of material on the internet for prompting,
10
with articles like "30 Prompts Everyone Has to Know" A lot of that has been focused on the ChatGPT web user interface,
11
which many people are using to do specific and often one -off tasks.
12
I think the power of LOM's large language models as a developer too, that is using API calls to LOMs to quickly build software applications.
13
I think that is still very underappreciated.
14
In fact, my team at AI Fund, which is a sister company to deep learning AI,
15
has been working with many startups on applying these technologies to many different applications.
16
It's been exciting to see what LOM APIs can enable developers to very quickly build.
17
So in this course, we'll share with you some of the possibilities for what you can do, as well as best practices for how you can do them.
18
There's a lot of material to cover.
19
First, you'll learn certain prompting best practices for software development.
20
Then we'll cover some common use cases, summarising, inferring, transforming, expanding.
21
And then you'll build a chatbot using an LLM.
22
Thank you.
23
We hope that this will spark your imagination about new applications that you can build.
24
So in the development of large language models or LLMs, there have been broadly two types of LLMs,
25
which I'm going to refer to as base LLMs and instruction -tuned LLMs.
26
So base LLM has been trained to predict the next word, based on text training data, often trained on large amount of data from the internet and other sources,
27
to figure out what's the next most likely word to follow.
28
So for example, if you were to prompt this, once upon a time there was a unicorn, it may complete this, that is, it may predict the next several words are,
29
that live in the magical forest of all unicorn friends.
30
But if you were to prompt us with what is the capital of France, then based on what articles on the internet might have,
31
it's quite possible that the base LM will complete this with, what is France's largest city, what is France's population and so on.
32
Because articles on the internet could quite plausibly be lists of quiz questions about the country of France.
33
Thank you.
34
In contrast, an instruction tuned LLM, which is where a lot of momentum of LLM research and practice has been going,
35
An instruction team has been trained to follow instructions.
36
So if you were to ask it, what is the capital of France is much more likely to output something like the capital of France is Paris
37
So the way that instruction -tuned OOMs are typically trained is, you start off with a base OOM that's been trained on a huge amount of text data,
38
and further train it, further fine tune it with inputs and outputs that are instructions and good attempts to follow those instructions.
39
And then often further refine using a technique called RLHF, reinforcement learning from human feedback,
40
to make the system better able to be helpful and follow instructions.
41
Because instruction TNLMs have been trained to be helpful, honest, and harmless.
42
So for example, they're less likely to output problematic texts, such as toxic outputs compared to base LLM.
43
a lot of the practical usage scenarios have been shifting toward instruction to,
44
you know, Some of the best practices you find on the internet may be more suited for a base LLM, but for most practical applications today,
45
we would recommend most people instead focus on instruction tuned LLMs, which are easier to use.
46
And also because of the work of OpenAI and other LLM companies becoming safer and more aligned.
47
So this course will focus on best practices for instruction to neural arms
48
which is what we recommend you use for most of your applications.
49
Before moving on, I just want to acknowledge the team from OpenAI
50
and DeepLearn .AI that had contributed to the materials that Ysia and I will be presenting.
51
I'm very grateful to Andrew Main, Joe Palermo, Boris Power, Ted Sanders, and Lillian Wang from OpenAI.
52
They were very involved with us brainstorming materials, vetting the materials to put together.
53
the curriculum for this short course.
54
And I'm also grateful on the deep learning side for the work of Jeff Lodwick, Eddie Hsu and Tommy Nelson.
55
So when you use an instruction tuned LLM, think of giving instructions to another person.
56
Say someone that's smart but doesn't know the specifics of your task.
57
So when an LM doesn't work, sometimes it's because the instructions weren't clear enough.
58
For example, if you were to say, "Please write me something about Alan Turing." Well, In addition to that, it can be helpful to be clear about whether you want
59
the text to focus on his scientific work or his personal life or his role in history or something else.
60
And if you specify what you want the tone of the text to be, should it take on the tone like a professional journalist would write?
61
Or is it more of a casual note
62
that you dash off to a friend that holds the OM generate what you want.
63
And of course, if you picture yourself asking, say a fresh college graduate to carry out this task for you,
64
if you can even specify what you want snippets of texts
65
they should read in advance to write this text about Alan Turing, then that even better sets up that fresh college grad for success to carry out this task for you.
66
So in the next video, you see examples of how to be clear and specific, which is an important principle of prompting LOMs.
67
And you also learn from Ezer a second principle of prompting, that is giving LDLM time to think.
68
So with that, let's go on to the next video.
📺 Mismo canal
✨ Recomendar Video
C11
747 oraciones
MIT Introduction to Deep Learning | 6.S191
Business EnglishUniversity StudentsWorking Adults
C11
125 oraciones
Introduction to statistical prediction models
Business EnglishUniversity StudentsWorking Adults
C12
145 oraciones
Most devs don't understand how LLM tokens work
British AccentBusiness EnglishUniversity StudentsWorking Adults
C15
107 oraciones
What is NLP? Neuro Linguistic Programming
Business EnglishUniversity StudentsWorking Adults
C11
111 oraciones
Spec-Driven Development: AI Assisted Coding Explained
Business EnglishUniversity StudentsWorking Adults
C11
74 oraciones
LangChain in 5 Minutes (Explained Clearly)
Business EnglishUniversity StudentsWorking Adults
C12
20 oraciones
Is prompt engineering dead? Advice on optimizing AI interactions | Amazon Web Services
Business EnglishUniversity StudentsWorking Adults
C14
32 oraciones
Gemini Robotics 2 brings whole body intelligence to robots
Business EnglishUniversity StudentsWorking Adults
C11
103 oraciones
Amazon Bedrock Tutorial | Getting Started with Amazon Bedrock | AI Model Access & Testing
Business EnglishUniversity StudentsWorking Adults
C12
82 oraciones
2168 - Move From B1 to C1 By Describing Your Favorite Tasks
American AccentBusiness EnglishInterview EnglishUniversity Students
C11
674 oraciones
FULL Data Science Mock Interview | SQL, Statistics, Business
Business EnglishInterview EnglishUniversity Students
Vocabulario y notas de pronunciación para esta lección
Este vídeo tiene 68 frases y 1082 palabras para practicar shadowing. La parte hablada dura 6:22. El hablante habla a un ritmo natural de unas 170 palabras por minuto, cercano a una conversación cotidiana. Solo el 81 % de las palabras está entre las 3.000 más comunes del inglés, por lo que el vocabulario es exigente.
Vocabulario clave de este vídeo
15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| instruction sustantivo | /ɪnˈstɹʌkʃən/ | instrucción |
| prompt verbo | /pɹɑmpt/ | llevar a, concitar |
| tune sustantivo | /tjuːn/ | melodía |
| output sustantivo | /ˈaʊtpʊt/ | producción, output |
| helpful adjetivo | /ˈhɛlp.fl̩/ | servicial, adyuvante |
| developer sustantivo | /dɪˈvɛləpɚ/ | desarrollador |
| thrill verbo | /θɹɪl/ | emocionar, excitar |
| tone sustantivo | /ˈtəʊn/ | tono |
| practical adjetivo | /ˈpɹæk.tɪ.kəl/ | práctico |
| grateful adjetivo | /ˈɡɹeɪtf(ə)l/ | agradecido |
| contribute verbo | /kənˈtɹɪb.jut/ | contribuir, aportar |
| principle sustantivo | /ˈpɹɪn.sɪ.pəl/ | principio |
| predict verbo | /pɹɪˈdɪkt/ | predecir |
| specify verbo | /ˈspɛs.əˌfaɪ/ | especificar, circunstanciar |
| unicorn sustantivo | /ˈju.nɪ.kɔɹn/ | unicornio |
Phrasal verbs que vas a escuchar
| Palabra | Pronunciación | Significado |
|---|---|---|
| carry out verbo | /ˈkæ.ɹi aʊt/ | llevar a cabo, llevar al cabo |
| figure out verbo | averiguar, captar | |
| put together verbo | construir, elaborar |
Gramática en este vídeo
Las estructuras que más usa el hablante, con las palabras exactas del vídeo:
| Estructura | En el vídeo |
|---|---|
| Present perfect continuous have/has been + -ing — una acción que empezó antes y continúa | has been teaching · has been working · has been going |
| Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora | has been focused · have been · has been trained |
| Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace | has been focused · has been trained · been trained |
| Oraciones de relativo who / which + oración — información extra sobre una persona o cosa | Fund, which is · LLM, which is · LLMs, which are |
Pronunciación a tener en cuenta
El hablante usa 14 contracciones y formas reducidas, como I'm, doesn't, we'll. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “sh” y “zh”: instruction /ɪnˈstɹʌkʃən/, contrast /kənˈt(ʃ)ɹæst/, casual /ˈkæʒ(u)əl/, imagination /ɪˌmæd͡ʒəˈneɪʃən/, dash /ˈdæʃ/
- Palabras largas — cuida el acento: developer /dɪˈvɛləpɚ/, imagination /ɪˌmæd͡ʒəˈneɪʃən/, curriculum /kəˈɹɪk.jə.ləm/, momentum /ˌmə(ʊ)ˈmɛntəm/, problematic /ˌpɹɑ.bləˈmæt.ɪk/
Cómo practicar con este vídeo
- Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
- Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
- Grábate y compara con el original, prestando atención a palabras como instruction, prompt, tune.
¿Qué es la Técnica de Shadowing?
Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.