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https://github.com/afondiel/ChatGPT-Prompt-Engineering-DeepLearningAI

ChatGPT Prompt Engineering for Developers Crash & Free Course by DeepLearning.AI
https://github.com/afondiel/ChatGPT-Prompt-Engineering-DeepLearningAI

agi anthropic bard chatbot chatgpt chatgpt-api claude cohere deeplearningai gemini generativeai gpt huggingface langchain llm mistral openai prompt prompt-engineering

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ChatGPT Prompt Engineering for Developers Crash & Free Course by DeepLearning.AI

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README 

        
# ChatGPT Prompt Engineering by DeepLearning.AI



## Overview



This crash & free course on ChatGPT Prompt Engineering is offered by [DeepLearning.AI](https://learn.deeplearning.ai/chatgpt-prompt-eng/lesson/1/introduction) and lectured by `Andrew Ng` and `Isa Fulford` from [openai](openai.com).



## Course Plan



- [Lesson1: Introduction](./l1-intro-notes.md)

- [Lesson2: Guidelines](./l2-guidelines-notes.md)

- [Lesson3: Iterative](./l3-iterative-prompt-dev.md)

- [Lesson4: Summarizing](./l4-summarizing.md)

- [Lesson5: Inferring](./l5-inferring.md)

- [Lesson6: Transforming](./l6-transforming.md)

- [Lesson7: Expanding](./l7-expanding.md)

- [Lesson8: Chatbot](./l8-chatbot.md)

- [Lesson9: Conclusion](./l9-conclusion.md)



> All notebook examples are available in the [lab](./lab/) folder.

## Setup



Load the API key and relevant Python libaries



```python

import openai

import os



from dotenv import load_dotenv, find_dotenv

_ = load_dotenv(find_dotenv())



openai.api_key  = os.getenv('OPENAI_API_KEY')

```



**Helper function**



- This function will make it easier to use prompts and look at the generated outputs:



It uses OpenAI's `gpt-3.5-turbo` model and the [chat completions endpoint](https://platform.openai.com/docs/guides/chat).



```python



def get_completion(prompt, model="gpt-3.5-turbo"):

    messages = [{"role": "user", "content": prompt}]

    response = openai.ChatCompletion.create(

        model=model,

        messages=messages,

        temperature=0, # this is the degree of randomness of the model's output

    )

    return response.choices[0].message["content"]

```

### Usage



```python

text = f"""

You should express what you want a model to do by \ 

providing instructions that are as clear and \ 

specific as you can possibly make them. \ 

This will guide the model towards the desired output, \ 

and reduce the chances of receiving irrelevant \ 

or incorrect responses. Don't confuse writing a \ 

clear prompt with writing a short prompt. \ 

In many cases, longer prompts provide more clarity \ 

and context for the model, which can lead to \ 

more detailed and relevant outputs.

"""

prompt = f"""

Summarize the text delimited by triple backticks \ 

into a single sentence.

```{text}```

"""

response = get_completion(prompt)

print(response)

```

#### Completion : 



```

Clear and specific instructions should be provided to guide a model towards the desired output, and longer prompts can provide more clarity and context for the model, leading to more detailed and relevant outputs.

```



## References



Main Course : 

- https://learn.deeplearning.ai/chatgpt-prompt-eng/lesson/1/introduction



Others short Free Courses available on DeepLearning.AI : 

- https://learn.deeplearning.ai/