LLMs in Action: Examples and Hobby Projects

Hello friends 👋

As we conclude our journey through the fascinating world of large language models (LLMs), it’s time to shift our focus from theory to practice. We delved into the inner workings of these models and explored the complex architecture that underlies them. Now let’s see how LLMs are applied in real-world scenarios, from revolutionizing industries to powering everyday applications.

In this final piece, we will not only show practical examples, but also introduce hobby projects that you can build yourself, whether you are a seasoned developer or just starting your AI journey. Get ready to see LLMs come to life in ways that inspire both innovation and creativity!

Quick review

Large Language Models (LLMs) have become one of the most impactful developments in artificial intelligence (AI). These models, such as GPT-4, PaLM and LLaMA, have revolutionized natural language processing (NLP) and have been integrated into various applications across industries. Using massive amounts of text data and advanced neural networks, LLMs can generate human-like text, understand context, and perform a wide range of language-related tasks.

LLMs are a type of AI model designed to understand, generate, and manipulate human language. They are based on deep learning techniques, especially transformer architectures, which allow them to process large amounts of data and learn complex patterns in text. These models have billions of parameters and are trained on various data sets, making them capable of understanding the nuances of language.

Key Features of LLMs:

• Contextual understanding: LLMs understand the meaning of words in context, allowing them to generate coherent sentences and paragraphs.
• Scalability: They scale to handle huge data sets and perform better as they get larger.
• Transfer learning: Once trained, LLMs can be refined for specific tasks without the need to retrain.

Notable LLMs and their applications

1. GPT-4 by OpenAI

Overview: GPT-4 is the fourth version of OpenAI’s Generative Pre-trained Transformer (GPT) series. It is a large-scale LLM known for its ability to generate text that closely mimics human language, engage in complex conversations, and perform a variety of NLP tasks.

Applications:

• Generate content: GPT-4 can generate articles, summaries, blog posts, and even creative writing such as poetry and fiction.
• Help with coding: Tools like GitHub Copilot use GPT models to represent and autocomplete code snippets, helping developers with programming tasks.
• Customer support: GPT-4 enables chatbots and virtual assistants to handle customer queries, reducing the need for human intervention.

2. PaLM by Google AI

Overview: Pathways Language Model (PaLM) is a multimodal language model developed by Google AI. PaLM is designed to process not only text, but also other types of data, such as images and audio, making it an advanced model in the field of AI.

Applications:

• Multimodal AI: PaLM is used in applications where understanding both visual and textual data is essential, such as image captioning and video analysis.
• Translation services: It offers advanced machine translation capabilities, improving the accuracy and fluency of multi-language translations.
• Healthcare: PaLM helps in the analysis of clinical data and provides suggestions and insights for medical professionals by analyzing patient records.

3. LLaMA by Meta (formerly Facebook)

Overview: LLaMA (Large Language Model Meta AI) is a highly efficient model of Meta designed for research purposes. It’s smaller than some of the largest models, but delivers impressive performance while using fewer computer resources.

Applications:

• Academic research: LLaMA is mainly used in academic and industrial research to study language models and understand their behavior.
• Tasks with few resources: Due to its efficiency, LLaMA is used in environments with limited computing power, such as mobile applications.
• Data augmentation: It helps generate synthetic data for training smaller models for specific tasks.

Key examples of LLM applications

1. Chatbots for customer service

LLMs like GPT-4 are widely used to power customer service chatbots. These AI-powered bots can understand customer questions, provide relevant answers, and escalate issues when necessary. They are used in industries such as e-commerce, banking and telecommunications to handle customer queries, reduce the workload for human agents and improve response times.

Example:

• Chatbots for banks: Banks like HSBC and Capital One use LLMs in their chatbots to help customers check their balances, make transactions, or answer general questions.

2. Content creation and SEO

LLMs transform content creation by automating the generation of blog posts, articles, product descriptions, and more. They can optimize content for search engines by suggesting keywords and writing SEO-friendly text.

Example:

• Blog automation: Companies like Jasper and Writesonic use GPT-based models to create marketing copy, blogs, and social media posts, allowing companies to maintain an active online presence without the need for a full-time content team.

3. Healthcare and medical research

LLMs have entered the healthcare industry, providing assistance in analyzing clinical records, proposing diagnoses and even aiding in drug discovery. The ability of LLMs to process large amounts of unstructured text makes them useful in research and patient care.

Example:

• IBM Watson Health: Although not a traditional LLM, IBM Watson Health uses NLP models to analyze medical literature and clinical data and help physicians with treatment suggestions based on existing research.

4. Coding and software development

LLMs like Codex (based on GPT-3) are used in tools like GitHub Copilot to help developers by suggesting code additions, detecting bugs, and generating code snippets from simple English descriptions.

Example:

• GitHub copilot: Copilot integrates with IDEs like Visual Studio Code and suggests relevant code in real time as developers write, reducing time spent on repetitive coding tasks and allowing them to focus on solving problems.

5. Legal research and drafting of documents

Law firms and legal departments use LLMs to automate legal research, draft documents and analyze case law. This speeds up legal workflows and allows for more thorough reviews of relevant legal precedents.

Example:

• Legal AI tools: Tools like Lexion and Casetext use LLMs to help lawyers draft contracts and analyze court documents, saving hours of manual work.

Challenges and Limitations of LLMs

Although LLMs have enormous potential, they also come with challenges:

1. Prejudice: LLMs can reflect biases present in their training data, leading to biased results.
2. Resource intensive: Training and running LLMs requires significant computing power, which can be expensive.
3. Ethical concerns: The use of LLMs in areas such as surveillance, disinformation or biased decision-making raises ethical issues that need to be addressed.
4. Limited understanding: Although LLMs are good at pattern recognition, they don’t really “understand” the meaning of the text the way humans do, which limits their ability to reason deeply.

Possibly Hobby projects based on large language models (LLMs)

Here are a few ideas for hobby projects related to LLMs in web development, machine learning, and cloud technologies:

1. Personal Spending Tracker (React + Flask + MongoDB)

• Build a web app that helps users manage their personal finances. Users can enter their daily expenses, set budgets, and view analytics such as monthly expenses and expenses by category.
• Features: User authentication, real-time data visualization (using Chart.js or D3.js), and cloud storage with MongoDB or Firebase.

2. Chatbot with React Frontend and LLM Backend

• Create a personal assistant chatbot using OpenAI’s GPT or an open-source LLM backend. The frontend can be built with React, while the backend offers LLM-powered answers.
• Features: User authentication, chatbot personalization and integration with APIs such as weather forecasts, reminders, etc.

3. Movie recommendation system using collaborative filtering

• Develop a movie recommendation engine using a machine learning model based on collaborative filtering. You can use Python libraries like Surprise or TensorFlow for model building and Flask or Django for the backend API.
• The frontend could be a React-based UI where users can browse movies, rate them, and get personalized recommendations.

4. Plant Disease Detection Using Deep Learning (React Native App)

• Build a mobile app that allows users to upload images of their plants, and the app will detect if the plant is suffering from a disease using a deep learning model trained on plant datasets.
• The mobile app can be built with React Native and the model can be hosted on AWS Lambda or another cloud service.

5. Weather dashboard with custom alerts

• Create a weather dashboard that pulls data from a public API like OpenWeather and presents real-time weather updates. You can also set up custom weather alerts (for example, you can send notifications when the temperature drops below a certain threshold).
• Features: Email or SMS notifications for weather alerts.

Sample code: Personal expense tracker

Below is a basic implementation of the Personal expense tracker use React for frontend and Flask for backend.

Backend (bottle):

from flask import Flask, jsonify, request
from flask_pymongo import PyMongo
from bson import ObjectId
import os

app = Flask(__name__)
app.config("MONGO_URI") = os.getenv("MONGO_URI", "mongodb://localhost:27017/expenses_db")
mongo = PyMongo(app)

@app.route('/expenses', methods=('GET'))
def get_expenses():
    expenses = list(mongo.db.expenses.find())
    for expense in expenses:
        expense('_id') = str(expense('_id'))
    return jsonify(expenses)

@app.route('/expenses', methods=('POST'))
def add_expense():
    data = request.json
    new_expense = {
        "title": data('title'),
        "amount": data('amount'),
        "category": data('category'),
        "date": data('date')
    }
    result = mongo.db.expenses.insert_one(new_expense)
    return jsonify({"id": str(result.inserted_id)}), 201

@app.route('/expenses/', methods=('DELETE'))
def delete_expense(id):
    mongo.db.expenses.delete_one({"_id": ObjectId(id)})
    return jsonify({"message": "Expense deleted"}), 200

if __name__ == "__main__":
    app.run(debug=True)

Frontend (response):

import React, { useState, useEffect } from 'react';
import axios from 'axios';

const ExpenseTracker = () => {
  const (expenses, setExpenses) = useState(());
  const (title, setTitle) = useState('');
  const (amount, setAmount) = useState('');
  const (category, setCategory) = useState('');
  const (date, setDate) = useState('');

  useEffect(() => {
    axios.get('/expenses')
      .then(response => setExpenses(response.data))
      .catch(error => console.error(error));
  }, ());

  const addExpense = () => {
    axios.post('/expenses', { title, amount, category, date })
      .then(response => {
        setExpenses((...expenses, { title, amount, category, date, _id: response.data.id }));
        setTitle('');
        setAmount('');
        setCategory('');
        setDate('');
      })
      .catch(error => console.error(error));
  };

  const deleteExpense = (id) => {
    axios.delete(`/expenses/${id}`)
      .then(() => setExpenses(expenses.filter(exp => exp._id !== id)))
      .catch(error => console.error(error));
  };

  return (
    div>
      h1>Expense Trackerh1>
      div>
        input type="text" value={title} onChange={e => setTitle(e.target.value)} placeholder="Title" />
        input type="number" value={amount} onChange={e => setAmount(e.target.value)} placeholder="Amount" />
        input type="text" value={category} onChange={e => setCategory(e.target.value)} placeholder="Category" />
        input type="date" value={date} onChange={e => setDate(e.target.value)} />
        button onClick={addExpense}>Add Expensebutton>
      div>
      ul>
        {expenses.map(exp => (
          li key={exp._id}>
            {exp.title} - {exp.amount} - {exp.category} - {exp.date}
            button onClick={() => deleteExpense(exp._id)}>Deletebutton>
          li>
        ))}
      ul>
    div>
  );
};

export default ExpenseTracker;

This project can be deployed using a cloud platform such as Heroku or AWS. You can expand this by adding more features such as user authentication, monthly reports, or by integrating it with a payment API to track expenses in real time.

Conclusion

Major language models like GPT-4, PaLM, and LLaMA have changed the way we approach language tasks in various areas, from customer support to content creation and healthcare. While there are challenges, ongoing research aims to address these limitations and expand the capabilities of LLMs in even more innovative ways. As these models continue to evolve, we can expect more advanced capabilities and broader adoption across industries. For more updates on my GenAI Journey, keep an eye on my LinkedIn and Dev.to profiles for our next articles.

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