Intro to LLMs (Large Language Models)
Overview
Large Language Models (LLMs) are the foundation of modern AI applications like ChatGPT, Claude, and others. In this lesson, you’ll learn what LLMs are, the difference between various providers like OpenAI and HuggingFace, and how to build dynamic prompts using Prompt Templates — a critical part of using LLMs effectively with LangChain.
Learning Objectives
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Understand what LLMs are and how they work
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Compare OpenAI and HuggingFace models
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Learn what Prompt Templates are and how to use them
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Create basic prompts to interact with LLMs
Section 1: What is a Large Language Model (LLM)?
An LLM is a deep learning model trained on a large text corpus to generate human-like language. These models can:
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Answer questions
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Write essays or code
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Summarize documents
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Translate text
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Perform logical reasoning
Popular examples:
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GPT-3.5 / GPT-4 (OpenAI)
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Claude (Anthropic)
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LLaMA (Meta)
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Falcon, Mistral, MPT, and others (Open-source)
Section 2: OpenAI vs HuggingFace Models
| Feature | OpenAI | HuggingFace Transformers |
|---|---|---|
| API Access | Cloud-based via OpenAI API | Run locally or via HuggingFace Hub |
| Popular Models | GPT-3.5, GPT-4, Whisper | BLOOM, Falcon, Mistral, CodeGen |
| Use Case Fit | Best for fast results and production | Best for open-source customization |
| Pricing | Paid, usage-based | Free/open-source or HuggingFace Inference API |
LangChain supports both: you can integrate OpenAI for production apps, and HuggingFace for offline/local testing.
Section 3: Introduction to Prompt Engineering
A prompt is the instruction you give the LLM to perform a task.
Example Prompt:
arduino
CopyEdit
Translate this English text to French: "How are you?"
You can dynamically format these using PromptTemplate in LangChain.
Section 4: What is a PromptTemplate in LangChain?
LangChain provides a utility called PromptTemplate to create prompts that accept variables.
Why use it?
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Reusable
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Parameterized
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Cleaner code
Example:
from langchain.prompts import PromptTemplate
template = "Translate the following text to {language}: {text}"
prompt = PromptTemplate(
input_variables=["language", "text"],
template=template
)
formatted_prompt = prompt.format(language="Spanish", text="Hello, how are you?")
print(formatted_prompt)
Output:
Translate the following text to Spanish: Hello, how are you?
Section 5: Try It with OpenAI and LangChain
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
import os
from dotenv import load_dotenv
load_dotenv()
llm = ChatOpenAI(temperature=0.7)
prompt = PromptTemplate(
input_variables=["topic"],
template="Explain {topic} to a 10-year-old."
)
chain = LLMChain(llm=llm, prompt=prompt)
print(chain.run("Quantum Physics"))
Mini Task
Build a prompt that asks the model to summarize any text. Example:
Summarize the following in one paragraph: {input_text}
