Complete AI Guide

1/17/2025 • AI TOOLS
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Welcome to your AI Guide – your ultimate companion to exploring the fascinating world of artificial intelligence. This guide is designed to empower you with practical knowledge and hands-on skills in key areas such as crafting effective prompts, mastering prompt engineering, building innovative AI projects, and discovering powerful AI tools. Whether you’re a beginner eager to dive into AI or a seasoned enthusiast looking to sharpen your expertise, this guide provides actionable insights and resources to unlock the true potential of AI in your creative and technical endeavors.

Contents

Prompt engineering guide

Contents

Understanding Prompt Engineering

Prompt engineering is the art and science of crafting effective inputs for AI language models like ChatGPT. It involves understanding both the capabilities and limitations of AI models to generate optimal responses. Success in prompt engineering requires:

  • Technical Understanding: Knowledge of how LLMs process and interpret text
  • Clear Communication: Ability to articulate requirements precisely
  • Iterative Refinement: Willingness to test and improve prompts
  • Context Awareness: Understanding of the model’s training boundaries

The Anatomy of an Effective Prompt

A well-structured prompt typically includes several key components:

  1. Context Setting:

    • Define the AI’s role (e.g., “Act as a senior software architect”)
    • Establish expertise level (e.g., “Explain this as if to a junior developer”)
    • Set the scope (e.g., “Focus on Python best practices”)

  2. Input Parameters:

    • User background information
    • Specific use case details
    • Relevant constraints
    • Technical requirements

  3. Clear Instructions:

    • Step-by-step task breakdown
    • Priority ordering
    • Dependencies between tasks
    • Expected deliverables

  4. Output Format:

    • Response structure (e.g., bullet points, numbered lists)
    • Required sections
    • Formatting preferences
    • Length constraints

Template: Creating Customized Learning Paths

Here’s a powerful prompt template for generating personalized learning experiences:

User Profile Section

  • Age
  • Education Level
  • Occupation
  • Field of Interest
  • Preferred Learning Style
  • Current Understanding Level
  • Key Goals
  • Available Learning Time

Enhanced Task Instructions

  1. Break down the main topic into structured subtopics

    • Create logical progression
    • Identify prerequisites
    • Set milestone markers
    • Include knowledge checkpoints

  2. Provide summaries for each section

    • Key concepts overview
    • Common pitfalls
    • Best practices
    • Real-world applications

  3. Suggest relevant resources

    • Official documentation
    • Tutorial links
    • Practice exercises
    • Community forums

  4. Maintain appropriate tone and engagement

    • Match user expertise level
    • Include motivational elements
    • Provide progress markers
    • Add interactive components

  5. Create a weekly learning schedule

    • Time-boxed activities
    • Progress tracking
    • Review sessions
    • Practice assignments

Real-World Example

Let’s look at how this template works for a software engineer learning machine learning:

Sample User Profile

  • Age: 25
  • Education: Undergraduate
  • Occupation: Software Engineer
  • Interest: Machine Learning
  • Learning Style: Hands-on
  • Level: Beginner
  • Goal: Build basic ML models
  • Time Available: 6 hours/week

Enhanced Learning Path

  1. Introduction to Machine Learning (Week 1-2)

    • Core concepts and definitions
      • Supervised vs unsupervised learning
      • Classification vs regression
      • Model training workflow
    • Types of ML approaches
      • Traditional algorithms
      • Neural networks
      • Deep learning basics
    • Practical exercises
      • Python basics for ML
      • NumPy and Pandas fundamentals
      • Basic data manipulation

  2. Core ML Concepts

    • Dataset management
    • Feature engineering
    • Data preprocessing

  3. Basic Algorithms

    • Linear regression
    • Decision trees
    • Hands-on implementation

  4. Evaluation Methods

    • Key metrics
    • Testing approaches
    • Model validation

  5. Practical Application

    • Real-world project
    • Implementation steps
    • Performance optimization

Advanced Prompt Engineering Techniques

  1. Chain of Thought Prompting

    • Break complex problems into steps
    • Show reasoning process
    • Include intermediate calculations
    • Validate each step

  2. Few-Shot Learning

    • Provide example pairs
    • Demonstrate patterns
    • Include edge cases
    • Show error handling

  3. Role-Based Prompting

    • Define expertise level
    • Set communication style
    • Establish authority
    • Maintain consistency

  4. Iterative Refinement

    • Start with basic prompts
    • Analyze responses
    • Identify improvements
    • Refine systematically

Common Pitfalls to Avoid

  1. Ambiguous Instructions

    • Unclear objectives
    • Missing context
    • Vague requirements
    • Inconsistent formatting

  2. Information Overload

    • Too many requirements
    • Excessive details
    • Complex structures
    • Conflicting instructions

Best Practices for Prompt Engineering

  1. Be specific and detailed

    ❌ "Write code for a sorting algorithm"
âś… "Write a Python implementation of the quicksort algorithm with detailed comments explaining the partition logic and time complexity"

  2. Use clear formatting

    ❌ "give me info about react hooks explain lifecycle methods and include code examples"
âś… "Explain React hooks with:
   - Brief introduction to each hook
   - Code examples for useState and useEffect
   - Comparison with lifecycle methods
   - Common use cases
   Please format the response with markdown and include syntax-highlighted code blocks."

  3. Break down complex requests

    ❌ "Create a full-stack web application"
âś… "Let's build a web application in steps:
   1. First, design the database schema for a user authentication system
   2. Then, create the API endpoints for user registration and login
   3. Finally, implement the frontend login form
   Please provide the solution for step 1 first."

  4. Include examples when needed

    ❌ "Help me write regex for email validation"
âś… "Help me write a regex pattern for email validation. Here are some test cases it should handle:
   Valid: user@domain.com, user.name@domain.co.uk
   Invalid: user@domain, @domain.com, user@.com"

  5. Specify output format preferences

    ❌ "Explain Docker concepts"
âś… "Explain Docker concepts in a table format with three columns:
   | Concept | Description | Example Command |
   Include: containers, images, volumes, and networks"

Conclusion

Effective prompt engineering is crucial for getting the most out of AI tools like ChatGPT. By following structured templates and understanding key components, you can create more effective prompts that generate better results.

Build AI Projects fast

If you’re looking to hone your software development skills and fast-track your growth into a highly proficient engineer, these project ideas will provide you with the right mix of foundational concepts, intermediate challenges, and advanced features. Here’s how to structure your learning journey, divided into beginner, intermediate, and advanced levels.

Table of Contents

Beginner Prompts

1. User Authentication System

Objective: Build a simple user authentication system using Supabase.
Features to Include:

  • Email and password login functionality
  • User signup, login, and logout
  • Password reset functionality via email

2. Basic To-Do App

Objective: Create a full-stack to-do list application using React (frontend) and Firebase (backend).
Features to Include:

  • Add, update, delete, and mark tasks as complete
  • Real-time database updates
  • User-friendly interface

3. Simple Blog

Objective: Develop a blog application for creating, editing, and deleting posts.
Tech Stack:

  • Backend: Node.js with Express
  • Database: MongoDB
  • Frontend: HTML/CSS

Intermediate Prompts

1. Sports Community App

Objective: Develop a sports community app using Supabase.
Features to Include:

  • User roles: Manager, Coach, and Student
  • Role-based access control
    • Managers can add team information
    • Coaches can schedule practice sessions
    • Students can view their schedules

2. E-commerce Shopping Cart

Objective: Build a shopping cart app using React, Node.js, Express, and MongoDB.
Features to Include:

  • Product dashboard
  • Add items to cart, update quantities, and calculate total cost
  • Persist cart data using sessions or a database

3. Recipe Manager for Restaurants

Objective: Create a restaurant management app.
Features to Include:

  • User and manager roles using Supabase authentication
  • Form for managers to add recipes
  • Customer interface to view recipes and add items to a shopping cart
  • Manager dashboard to view customer orders

Advanced Prompts

1. Collaborative Task Manager

Objective: Build a task manager with real-time features.
Features to Include:

  • Real-time updates using WebSockets
  • User roles: Admin, Manager, and Team Member
  • Supabase integration for real-time updates and user authentication

2. Social Media App for Athletes

Objective: Create a social media app for athletes using Next.js and Firebase.
Features to Include:

  • User profiles, photo uploads, post likes, and comments
  • Infinite scrolling for posts
  • Real-time notifications using Firebase Cloud Messaging

3. Restaurant Analytics Dashboard

Objective: Extend the restaurant management app with advanced analytics.
Features to Include:

  • Analytics for order trends, popular dishes, and revenue
  • Data visualization using Chart.js or D3.js
  • Real-time notifications for managers when new orders are placed

4. E-commerce Recommendation System

Objective: Enhance the e-commerce app with machine learning-powered recommendations.
Features to Include:

  • Personalized product suggestions based on browsing and purchase history
  • Machine learning models hosted on AWS Lambda

Additional Project Ideas

  • Online Course Platform: Allow instructors to upload courses, and students to enroll and take lessons. Include a discussion forum.
  • Event Management System: Create a platform where users can create and RSVP to events, with admin controls for event approvals.
  • AI Chatbot Integration: Add an AI chatbot to any app using OpenAI GPT APIs to assist users with queries.

Why These Prompts Are Valuable

These projects will:

  • Teach you the core technologies in software development.
  • Help you integrate advanced features like authentication, real-time updates, and analytics.
  • Provide practical experience, preparing you for real-world challenges.

AI projects built with Claude

Image comparison tool https://claude.site/artifacts/a49309c9-49ec-4888-a790-891894001525

Rubics cube simulator https://claude.site/artifacts/0a4a1e14-85d8-4169-b251-6ceebda740cb

Bloom filter visualization https://claude.site/artifacts/2e673219-d056-44b8-9642-b0bbee928c54

Self playing snake game https://claude.site/artifacts/5574c1eb-ea43-4e20-9d89-c96b9d4cf1de

Reddit thread formatter https://claude.site/artifacts/6892d6ff-5f2e-4731-bb52-14fd3ec781db

Bitcoin game https://claude.site/artifacts/cf93dea8-1b44-4c44-8ff3-a2eef2a7d8cd

Music drum pad https://claude.site/artifacts/091a4c98-f9fc-4601-ae3d-29e3382cd5a3

TDEE, Macronutrient, and BMI Calculator https://claude.site/artifacts/bab072fe-4873-4a5f-b626-a076341c2d76

Stock Option Call Analyzer https://claude.site/artifacts/cd452263-d93a-4506-94b6-6dcb9681512f

Flappy bird game https://x.com/NickADobos/status/1803837860635627558

Sudoku solver https://claude.site/artifacts/22a509b7-5f6a-478a-b56c-298898c3cba6

Python interpreter https://claude.site/artifacts/3fdd108a-4937-4ca6-b2f4-3cf370fec57c

Drum synth pad https://claude.site/artifacts/4a0693bb-db93-4e81-8342-fd0921ae49b5

Diff checker https://claude.site/artifacts/020cf820-7c4b-406b-aeb5-e876337cd6af

Double pendulum simulator https://claude.site/artifacts/33451efb-3ddd-496e-95f9-3cc08c6c72de

By tackling these prompts, you’ll progressively master the skills needed to become a 100x engineer.

Building an AI SaaS

Table of Contents

Project Overview

We’ll build an AI chat application with:

  • Free tier (5 messages)
  • Authentication
  • Credits system
  • Payment integration
  • Chat history
  • User profiles

Core Features Implementation

1. Authentication Flow

// src/contexts/AuthContext.tsx
import { createContext, useContext, useState } from 'react';
import { supabase } from '../lib/supabase';

export const AuthContext = createContext({});

export function AuthProvider({ children }) {
  const [user, setUser] = useState(null);
  const [session, setSession] = useState(null);

  // Check for free messages before requiring auth
  const checkAuthRequirement = async (messageCount) => {
    if (messageCount >= 5 && !session) {
      return true; // Require auth
    }
    return false;
  };

  return (
    <AuthContext.Provider value={{ user, session, checkAuthRequirement }}>
      {children}
    </AuthContext.Provider>
  );
}

2. Chat Implementation

// src/components/Chat.tsx
import { useState, useEffect } from 'react';
import { supabase } from '../lib/supabase';
import { useAuth } from '../contexts/AuthContext';

export default function Chat() {
  const [messages, setMessages] = useState([]);
  const [messageCount, setMessageCount] = useState(0);
  const { session, checkAuthRequirement } = useAuth();

  const handleSendMessage = async (content) => {
    // Check free tier limit
    const requireAuth = await checkAuthRequirement(messageCount);
    if (requireAuth) {
      showAuthModal();
      return;
    }

    // Process message with OpenAI
    const response = await processWithAI(content);
    
    // Save to database if authenticated
    if (session) {
      await saveToDatabase(content, response);
    }
    
    setMessageCount(prev => prev + 1);
  };
}

3. OpenAI Integration

// src/lib/openai.ts
import { Configuration, OpenAIApi } from 'openai';

const configuration = new Configuration({
  apiKey: process.env.OPENAI_API_KEY,
});

const openai = new OpenAIApi(configuration);

export async function processWithAI(content: string) {
  const completion = await openai.createChatCompletion({
    model: "gpt-3.5-turbo",
    messages: [{ role: "user", content }],
  });

  return completion.data.choices[0].message.content;
}

4. Credits System

// src/lib/credits.ts
export async function checkCredits(userId: string) {
  const { data: credits } = await supabase
    .from('credits')
    .select('amount')
    .eq('user_id', userId)
    .single();

  return credits?.amount || 0;
}

export async function deductCredit(userId: string) {
  const { data, error } = await supabase
    .from('credits')
    .update({ amount: credits - 1 })
    .eq('user_id', userId);

  return !error;
}

5. Stripe Integration

// src/lib/stripe.ts
import Stripe from 'stripe';

const stripe = new Stripe(process.env.STRIPE_SECRET_KEY);

export async function createCheckoutSession(userId: string, priceId: string) {
  const session = await stripe.checkout.sessions.create({
    line_items: [{
      price: priceId,
      quantity: 1,
    }],
    mode: 'payment',
    success_url: `${process.env.DOMAIN}/success?session_id={CHECKOUT_SESSION_ID}`,
    cancel_url: `${process.env.DOMAIN}/cancel`,
    metadata: {
      userId,
    },
  });

  return session;
}

Database Schema

-- Users table
create table users (
  id uuid references auth.users primary key,
  email text,
  credits integer default 0
);

-- Chats table
create table chats (
  id uuid primary key default uuid_generate_v4(),
  user_id uuid references users(id),
  content text,
  response text,
  created_at timestamp with time zone default timezone('utc'::text, now())
);

-- Credits table
create table credits (
  id uuid primary key default uuid_generate_v4(),
  user_id uuid references users(id),
  amount integer default 0,
  updated_at timestamp with time zone default timezone('utc'::text, now())
);

User Interface Components

Profile Section

// src/components/UserProfile.tsx
export default function UserProfile() {
  const { user } = useAuth();
  const [credits, setCredits] = useState(0);
  const [chats, setChats] = useState([]);

  useEffect(() => {
    if (user) {
      fetchUserData();
    }
  }, [user]);

  return (
    <div className="p-6">
      <h2>Profile</h2>
      <div>Credits: {credits}</div>
      <div>Chat History</div>
      {chats.map(chat => (
        <ChatItem key={chat.id} chat={chat} />
      ))}
    </div>
  );
}

Credits Purchase UI

// src/components/PurchaseCredits.tsx
export default function PurchaseCredits() {
  const plans = [
    { credits: 100, price: 10, id: 'price_100' },
    { credits: 500, price: 40, id: 'price_500' },
    { credits: 1000, price: 70, id: 'price_1000' },
  ];

  const handlePurchase = async (priceId) => {
    const session = await createCheckoutSession(user.id, priceId);
    window.location.href = session.url;
  };

  return (
    <div className="grid grid-cols-3 gap-6">
      {plans.map(plan => (
        <PricingCard
          key={plan.id}
          plan={plan}
          onPurchase={() => handlePurchase(plan.id)}
        />
      ))}
    </div>
  );
}

Best Practices & Tips

  1. State Management

    • Use contexts for global state
    • Keep chat state persistent
    • Handle loading states properly

  2. Error Handling

    • Implement proper error boundaries
    • Show user-friendly error messages
    • Log errors for debugging

  3. Performance

    • Implement pagination for chat history
    • Cache API responses
    • Optimize database queries

  4. Security

    • Secure API endpoints
    • Validate user inputs
    • Protect sensitive data

Common Issues & Solutions

  1. Chat Persistence
// Save chat state in localStorage
useEffect(() => {
  const savedChats = localStorage.getItem('chats');
  if (savedChats) {
    setMessages(JSON.parse(savedChats));
  }
}, []);
  1. Authentication Modal
// Close modal after successful auth
useEffect(() => {
  if (session) {
    setShowAuthModal(false);
  }
}, [session]);
  1. Credit System
// Check credits before API call
const handleChat = async () => {
  const hasCredits = await checkCredits(user.id);
  if (!hasCredits) {
    showPurchaseModal();
    return;
  }
  // Process chat...
};

Technical Stack

Here’s our complete technical stack:

  • Frontend: Next.js 14 with App Router
  • Backend: Edge Functions (Vercel/Cloudflare)
  • Database: Supabase (PostgreSQL)
  • Authentication: Supabase Auth
  • Payments: Stripe
  • AI Provider: OpenAI
  • Analytics: PostHog
  • Monitoring: Sentry
  • Testing: Jest + Playwright

Development Environment Setup

# Initial setup
npm create next-app@latest ai-saas --typescript --tailwind
cd ai-saas

# Install dependencies
npm install @supabase/supabase-js stripe openai @vercel/analytics
npm install @tailwindcss/typography @tailwindcss/forms --save-dev

# Environment variables
cp .env.example .env.local

Advanced OpenAI Integration

import { Configuration, OpenAIApi } from 'openai';
import { rateLimit } from '../utils/rate-limit';

export class AIService {
  private static instance: AIService;
  private openai: OpenAIApi;
  
  private constructor() {
    this.openai = new OpenAIApi(new Configuration({
      apiKey: process.env.OPENAI_API_KEY,
    }));
  }

  static getInstance(): AIService {
    if (!AIService.instance) {
      AIService.instance = new AIService();
    }
    return AIService.instance;
  }

  async processMessage(content: string, context: string[] = []) {
    // Rate limiting check
    const identifier = context[0]?.userId || 'anonymous';
    const rateLimitResult = await rateLimit(identifier);
    
    if (!rateLimitResult.success) {
      throw new Error('Rate limit exceeded');
    }

    try {
      const completion = await this.openai.createChatCompletion({
        model: "gpt-3.5-turbo",
        messages: [
          { role: "system", content: "You are a helpful AI assistant." },
          ...context.map(msg => ({ role: "user", content: msg })),
          { role: "user", content }
        ],
        temperature: 0.7,
        max_tokens: 500,
        presence_penalty: 0.6,
      });

      return completion.data.choices[0].message.content;
    } catch (error) {
      console.error('OpenAI API Error:', error);
      throw new Error('Failed to process message');
    }
  }
}

Rate Limiting Implementation

import { Redis } from '@upstash/redis';

const redis = new Redis({
  url: process.env.UPSTASH_REDIS_URL,
  token: process.env.UPSTASH_REDIS_TOKEN,
});

export async function rateLimit(identifier: string, limit = 10, window = 60) {
  const key = `rate_limit:${identifier}`;
  
  try {
    const requests = await redis.incr(key);
    
    if (requests === 1) {
      await redis.expire(key, window);
    }

    return {
      success: requests <= limit,
      remaining: Math.max(0, limit - requests),
    };
  } catch (error) {
    console.error('Rate limiting error:', error);
    return { success: true, remaining: limit }; // Fail open
  }
}

Analytics Integration

import posthog from 'posthog-js';

export class Analytics {
  static init() {
    if (typeof window !== 'undefined') {
      posthog.init(process.env.NEXT_PUBLIC_POSTHOG_KEY, {
        api_host: process.env.NEXT_PUBLIC_POSTHOG_HOST,
      });
    }
  }

  static trackEvent(event: string, properties?: Record<string, any>) {
    posthog.capture(event, properties);
  }

  static identifyUser(userId: string, traits?: Record<string, any>) {
    posthog.identify(userId, traits);
  }
}

Testing Strategy

import { render, fireEvent, waitFor } from '@testing-library/react';
import Chat from '../components/Chat';

describe('Chat Component', () => {
  it('should handle message sending', async () => {
    const { getByRole, getByText } = render(<Chat />);
    
    const input = getByRole('textbox');
    const sendButton = getByRole('button', { name: /send/i });

    fireEvent.change(input, { target: { value: 'Hello AI' } });
    fireEvent.click(sendButton);

    await waitFor(() => {
      expect(getByText(/Hello AI/)).toBeInTheDocument();
    });
  });
});

Deployment Guide

{
  "env": {
    "OPENAI_API_KEY": "@openai_api_key",
    "STRIPE_SECRET_KEY": "@stripe_secret_key",
    "SUPABASE_URL": "@supabase_url",
    "SUPABASE_ANON_KEY": "@supabase_anon_key"
  },
  "build": {
    "env": {
      "NEXT_PUBLIC_SUPABASE_URL": "@supabase_url",
      "NEXT_PUBLIC_SUPABASE_ANON_KEY": "@supabase_anon_key"
    }
  }
}

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