Unlocking the Power of Prompt Engineering in Chatbots

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Welcome, fellow developers and AI enthusiasts! Today, we're diving deep into the world of Prompt Engineering. If you've ever dreamed of conversing with a machine as though it were a human, this guide is your golden ticket. We're not just talking about any chatbot – we're building one that thinks and responds with the nuance of a human mind.

Setting the Stage for Your AI Masterpiece[edit | edit source]

Imagine we're artists, and our canvas is the digital space. Before we unleash our creativity, let's get our studio ready. We're setting up shop in Amazon SageMaker Studio Lab – a haven for machine learning development that won't cost you a dime. It's here that our chatbot will take its first breath.

Gathering Your Tools[edit | edit source]

Before we start sculpting our chatbot, we need the right tools. Think of these libraries as our paintbrushes:

  • torch: The backbone of our AI, PyTorch will help bring our chatbot to life.
  • transformers: This is where the magic happens. Hugging Face's library is like the spellbook for our AI wizardry.
%pip install torch==1.13.1 transformers==4.27.4

With our environment prepped and our libraries in place, it's time to pick our muse – the Large Language Model (LLM) that will power our conversations.

Choosing Your Muse: The Large Language Model[edit | edit source]

model_name = "google/flan-t5-xl"

Our model of choice is the equivalent of picking a fine vintage – robust and full-bodied. The flan-T5-xl from Google's cellar is a connoisseur's pick, capable of understanding and generating human-like text.

The Art of Prompting[edit | edit source]

Prompt engineering is where we truly become artists. Our prompts are the strokes that guide our chatbot's responses. Let's begin with a simple "hello":

print(causal_prediction("My name is Mike."))

Every time you run this, the chatbot will dance to a different tune. It's unpredictable, yet fascinating.

The Zero-Shot: A Single Stroke[edit | edit source]

This is where we ask our chatbot to perform a task without any prior examples. It's like asking a painter to create a masterpiece without ever having seen a brush.

print(causal_prediction("""Summarize my text:
Today I went to the beach and saw a whale swimming in the sea. I had a great day and ate ice cream.
"""))

One-Shot and Few-Shot: Guiding the Brush[edit | edit source]

Here, we give our chatbot an example to follow, setting the stage for its creativity:

print(causal_prediction("""Summarize my text:
I visited Paris and saw the Eiffel Tower. The city's rich history made it a truly unforgettable experience.
Summary: My visit to Paris was unforgettable
"""))

Crafting a Conversationalist: The Chatbot[edit | edit source]

Now, let's breathe life into our chatbot with the power of Few-Shot Learning. We feed it a bit of history, helping it understand the rhythm and flow of human dialogue:

print(causal_prediction("""
This is a friendly, safe chatbot session between a user and a computer called Chatbot.
Mike:My name is Mike.
Chatbot:Hello Mike, I am Chatbot and I offer wise advice on technical issues.
"""))

With each prompt, our chatbot learns, adapts, and becomes more human.

Remembering the Past: Maintaining Chat History[edit | edit source]

Our chatbot's memory is its sketchbook, remembering past strokes to inform the next. By maintaining a conversation history, we create a chatbot that not only talks but remembers:

initial_prompt = [
    "User: What color is the Tardis?",
    "Chatbot: The Tardis is Dr Who's time travel machine, and is blue."
]

Your Chatbot, Your Rules[edit | edit source]

Feel like changing the model? Go ahead! Swap out the model_name and watch your chatbot transform. Remember to clear the cache (rm -rf ~/.cache/huggingface) to make room for new ideas.

Engage with Your Creation[edit | edit source]

Now for the grand finale. Let's talk to our chatbot. Ask it about the capital of Australia, or the color of the sky – it's all fair game. Every response is a step closer to a chatbot that's indistinguishable from a human.

input_field = widgets.Text(placeholder='Type your message here...')
send_button = widgets.Button(description='Send')

And there you have it! You've just created not just a chatbot, but a digital being capable of conversation that's as rich and complex as any human's. So, what will you ask

About This Page

This page was last edited on 28 November 2023, at 00:20.