How I built a production-ready AI application that generates SEO-optimized blog posts in 30 seconds
Why I Built This
I'll be honest with you - I've spent countless hours staring at blank screens, struggling to write blog posts. As a developer who loves building things but finds writing challenging, I often thought: "What if I could generate high-quality blog drafts in seconds?"
That question led me down a rabbit hole that resulted in building a complete AI-powered blog generator from scratch. This isn't just another AI wrapper - it's a full-stack application with custom prompt engineering, SEO optimization, and a beautiful user interface.
## Who This Is For
This article is for you if:
- You’re a backend developer curious about AI integration
- You want to see how AI fits into a real production system
- You care about structure, validation, and maintainability
- You’re learning React but primarily think in backend terms
What we're building:
- AI-powered blog generation (600-2500 words)
- Multiple writing tones (Professional, Casual, Technical, Educational)
- Automatic SEO scoring and keyword optimization
- PostgreSQL database for content management
- Beautiful, responsive React UI with Tailwind CSS
This is Part 1 of a two-part series. Today, we'll dive deep into the development process. Part 2 will cover deployment to production.
Tech Stack: Why These Choices?
Let me walk you through my technical decisions and the reasoning behind them.
Backend: FastAPI
I chose FastAPI over Flask or Django for several compelling reasons:
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
# Type-safe, auto-documented, and blazing fast
class BlogRequest(BaseModel):
topic: str
tone: str = "professional"
length: str = "medium"
keywords: str | None = None
@app.post("/generate")
async def generate_blog(request: BlogRequest):
# FastAPI handles validation automatically
return await ai_service.generate(request)
Why FastAPI?
- Performance: Built on Starlette, it's one of the fastest Python frameworks
- Auto Docs: Interactive API documentation (Swagger UI) out of the box
- Type Safety: Pydantic validation catches errors before they happen
- Modern: Native async/await support, perfect for AI API calls
- Developer Experience: Clear error messages and intuitive API design
Frontend: React + Vite + Tailwind
React for the component-based architecture and ecosystem.
Vite instead of Create React App because:
- Instant server start
- Lightning-fast hot module replacement
- Optimized production build
Tailwind CSS for rapid, maintainable styling:
// Clean, utility-first styling
Generate Blog Post
AI: Hugging Face Transformers
I chose Hugging Face over OpenAI for several reasons:
- Cost: More affordable for experimentation
- Open Models: Access to open-source models like Llama
- Control: Full control over model selection and parameters
- Transparency: Clear understanding of what's happening under the hood
Architecture: How It All Fits Together
Here's the high-level architecture:
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ React Frontend │◄───────►│ FastAPI Backend │◄───────►│ PostgreSQL │
│ (Port 5173) │ HTTP │ (Port 8000) │ ORM │ Database │
│ │ │ │ │ │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│
│ HTTPS
▼
┌──────────────────┐
│ Hugging Face │
│ Inference API │
│ (Llama 3.3 70B) │
└──────────────────┘
Data Flow:
- User fills form → Frontend validation
- POST request to
/api/v1/generate→ Backend receives data - Backend builds optimized prompt → Calls Hugging Face API
- AI generates content → Backend processes and cleans output
- Calculate SEO score → Save to PostgreSQL
- Return formatted blog → Frontend displays result
Backend Deep Dive
Project Structure
I organized the backend following a clean architecture pattern:
backend/
├── app/
│ ├── main.py # FastAPI app initialization
│ ├── config.py # Environment configuration
│ ├── database.py # Database connection
│ ├── models/
│ │ └── blog.py # SQLAlchemy models
│ ├── schemas/
│ │ └── blog.py # Pydantic schemas
│ ├── api/
│ │ └── routes.py # API endpoints
│ ├── services/
│ │ ├── ai_service.py # Hugging Face integration
│ │ └── seo_service.py # SEO scoring logic
│ └── utils/
│ └── post_processor.py # Content processing
└── requirements.txt
1. Database Models with SQLAlchemy
First, I defined the database schema for storing blog posts:
# app/models/blog.py
from sqlalchemy import Column, Integer, String, Text, DateTime, Float
from sqlalchemy.sql import func
from app.database import Base
class BlogPost(Base):
__tablename__ = "blog_posts"
id = Column(Integer, primary_key=True, index=True)
topic = Column(String(500), nullable=False, index=True)
tone = Column(String(50), nullable=False)
length = Column(String(20), nullable=False)
keywords = Column(Text, nullable=True)
title = Column(String(500), nullable=True)
content = Column(Text, nullable=False)
seo_score = Column(Float, nullable=True)
word_count = Column(Integer, nullable=True)
created_at = Column(DateTime(timezone=True), server_default=func.now())
Why this structure?
- Indexed fields (
topic,id) for faster queries - Flexible
keywordsas Text for comma-separated values - Timestamps for tracking creation
- SEO score as Float for decimal precision
2. Pydantic Schemas for Validation
Type-safe request/response models:
# app/schemas/blog.py
from pydantic import BaseModel, Field
from typing import Optional
class BlogGenerateRequest(BaseModel):
topic: str = Field(..., min_length=5, max_length=500)
tone: str = Field(default="professional")
length: str = Field(default="medium")
keywords: Optional[str] = Field(None)
class Config:
json_schema_extra = {
"example": {
"topic": "The Future of AI",
"tone": "professional",
"length": "medium",
"keywords": "AI, machine learning, automation"
}
}
class BlogResponse(BaseModel):
id: int
topic: str
title: str
content: str
seo_score: float
word_count: int
created_at: datetime
class Config:
from_attributes = True # Enable ORM mode
The beauty of Pydantic:
- Automatic validation on incoming requests
- Clear error messages if validation fails
- Auto-generated API documentation
- Type hints everywhere
3. AI Service: The Heart of the Application
This is where the magic happens. Here's my Hugging Face integration:
# app/services/ai_service.py
import requests
import time
from typing import Dict
class HuggingFaceService:
def __init__(self):
self.api_url = "https://router.huggingface.co/v1/chat/completions"
self.headers = {
"Authorization": f"Bearer {settings.HUGGINGFACE_API_KEY}",
"Content-Type": "application/json"
}
def generate_blog(self, topic: str, tone: str, length: str,
keywords: str = None) -> Dict:
"""Main entry point for blog generation"""
# 1. Build optimized prompt
prompt = self._build_prompt(topic, tone, length, keywords)
# 2. Call Hugging Face API
raw_content = self._call_api(prompt)
# 3. Post-process content
cleaned = self.post_processor.clean_content(raw_content)
title, content = self.post_processor.extract_title_and_content(
cleaned, topic
)
# 4. Calculate metrics
word_count = self.post_processor.count_words(content)
seo_score = self.seo_service.calculate_score(
content, title, keywords
)
return {
"title": title,
"content": content,
"word_count": word_count,
"seo_score": seo_score
}
Prompt Engineering: The Secret Sauce
Building effective prompts was crucial. Here's my approach:
def _build_prompt(self, topic: str, tone: str, length: str,
keywords: str = None) -> str:
"""Construct optimized prompt for blog generation"""
tone_map = {
"professional": "authoritative, polished, business-appropriate",
"casual": "friendly, conversational, relatable",
"technical": "detailed, precise, technically accurate",
"educational": "clear, informative, easy to understand"
}
length_map = {
"short": "600-800 words",
"medium": "1000-1500 words",
"long": "1800-2500 words"
}
return f"""You are an expert blog writer. Write a comprehensive blog post.
**Topic:** {topic}
**Tone:** {tone_map.get(tone)}
**Target Length:** {length_map.get(length)}
{f"**Keywords:** {keywords}" if keywords else ""}
**Requirements:**
1. Create an engaging, SEO-friendly title
2. Write a compelling introduction
3. Use clear subheadings (## for H2)
4. Include practical examples
5. End with a strong conclusion
**Format:**
# [Title]
[Introduction]
## [Section 1]
[Content]
## [Section 2]
[Content]
## Conclusion
[Summary]
Write the blog post now:"""
Why this prompt works:
- Clear structure and expectations
- Specific tone guidance
- Explicit formatting requirements
- Target word count for consistency
- Keyword integration instructions
API Call with Retry Logic
Hugging Face models can take time to load (503 errors). Here's my retry logic:
def _call_api(self, prompt: str, max_retries: int = 3) -> str:
"""Call HF API with retry logic"""
payload = {
"model": "meta-llama/Llama-3.3-70B-Instruct",
"messages": [
{
"role": "system",
"content": "You are an expert blog writer."
},
{
"role": "user",
"content": prompt
}
],
"max_tokens": 2500,
"temperature": 0.7,
"top_p": 0.9
}
for attempt in range(max_retries):
try:
response = requests.post(
self.api_url,
headers=self.headers,
json=payload,
timeout=120 # 2 minute timeout
)
# Model loading, wait and retry
if response.status_code == 503:
print(f"Model loading... waiting 30 seconds")
time.sleep(30)
continue
response.raise_for_status()
result = response.json()
return result["choices"][0]["message"]["content"]
except requests.exceptions.Timeout:
if attempt < max_retries - 1:
time.sleep(10)
continue
raise Exception("Request timed out")
raise Exception("Failed after multiple retries")
Key points:
- 503 handling for model warm-up
- Exponential backoff between retries
- Proper timeout handling
- Clear error messages
4. SEO Scoring Algorithm
I built a custom SEO scoring system that evaluates multiple factors:
# app/services/seo_service.py
import re
class SEOService:
@staticmethod
def calculate_score(content: str, title: str,
keywords: str = None) -> float:
"""Calculate SEO score (0-100)"""
score = 0.0
# 1. Word Count Analysis (25 points)
word_count = len(re.findall(r'\b\w+\b', content))
if 800 <= word_count <= 2500:
score += 25
elif 600 <= word_count < 800:
score += 18
elif word_count >= 500:
score += 12
# 2. Title Optimization (15 points)
if title:
title_length = len(title)
if 40 <= title_length <= 70:
score += 15
elif 30 <= title_length < 40:
score += 10
elif title_length > 0:
score += 5
# 3. Heading Structure (20 points)
h2_count = len(re.findall(r'##\s+', content))
h3_count = len(re.findall(r'###\s+', content))
total_headings = h2_count + h3_count
if 3 <= total_headings <= 8:
score += 20
elif 2 <= total_headings <= 10:
score += 15
elif total_headings > 0:
score += 8
# 4. Keyword Optimization (25 points)
if keywords:
keyword_list = [k.strip().lower()
for k in keywords.split(',')]
content_lower = content.lower()
title_lower = title.lower() if title else ""
# Keywords in content
keywords_in_content = sum(
1 for kw in keyword_list if kw in content_lower
)
if keywords_in_content >= len(keyword_list):
score += 15
elif keywords_in_content > 0:
score += 8
# Keywords in title
keywords_in_title = sum(
1 for kw in keyword_list if kw in title_lower
)
if keywords_in_title > 0:
score += 10
else:
score += 12 # Partial credit
# 5. Content Structure (15 points)
has_intro = bool(re.search(
r'(introduction|overview)',
content.lower()[:500]
))
has_conclusion = bool(re.search(
r'(conclusion|summary|final)',
content.lower()[-500:]
))
if has_intro:
score += 7
if has_conclusion:
score += 8
return min(round(score, 2), 100.0)
Scoring breakdown:
- 25 points: Optimal word count (800-2500)
- 15 points: Title length optimization
- 20 points: Proper heading structure
- 25 points: Keyword usage and placement
- 15 points: Content structure (intro/conclusion)
This gives us a fair, multi-dimensional quality score.
5. Post-Processing Pipeline
Raw AI output needs cleaning. Here's my post-processor:
# app/utils/post_processor.py
import re
from typing import Tuple
class PostProcessor:
@staticmethod
def clean_content(text: str) -> str:
"""Clean and format generated text"""
if not text:
return ""
# Remove excessive whitespace
text = re.sub(r'\n{3,}', '\n\n', text)
text = re.sub(r' {2,}', ' ', text)
return text.strip()
@staticmethod
def extract_title_and_content(text: str,
fallback_topic: str) -> Tuple[str, str]:
"""Extract title from content"""
if not text:
return f"{fallback_topic}: A Comprehensive Guide", ""
lines = text.split('\n')
title = None
content_lines = []
for line in lines:
line = line.strip()
# Look for title (lines starting with #)
if line.startswith('# ') and not title:
title = line.replace('# ', '').strip()
elif line:
content_lines.append(line)
# Fallback title if none found
if not title:
title = f"{fallback_topic}: A Comprehensive Guide"
content = '\n\n'.join(content_lines) if content_lines else text
return title, content
@staticmethod
def count_words(text: str) -> int:
"""Count words accurately"""
if not text:
return 0
words = re.findall(r'\b\w+\b', text)
return len(words)
Why post-processing matters:
- AI output isn't always perfectly formatted
- Need to extract structured data (title, content)
- Clean up artifacts and inconsistencies
- Ensure consistent formatting
6. API Routes: Putting It All Together
Finally, the API endpoints that tie everything together:
# app/api/routes.py
from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy.orm import Session
router = APIRouter()
@router.post("/generate", response_model=BlogResponse, status_code=201)
def generate_blog(
request: BlogGenerateRequest,
db: Session = Depends(get_db)
):
"""Generate a new blog post using AI"""
try:
# Generate content
result = hf_service.generate_blog(
topic=request.topic,
tone=request.tone,
length=request.length,
keywords=request.keywords
)
# Save to database
blog_post = BlogPost(
topic=request.topic,
tone=request.tone,
length=request.length,
keywords=request.keywords,
title=result["title"],
content=result["content"],
word_count=result["word_count"],
seo_score=result["seo_score"]
)
db.add(blog_post)
db.commit()
db.refresh(blog_post)
return blog_post
except Exception as e:
db.rollback()
raise HTTPException(status_code=500, detail=str(e))
@router.get("/blogs", response_model=BlogListResponse)
def list_blogs(
skip: int = 0,
limit: int = 20,
db: Session = Depends(get_db)
):
"""Get list of all generated blogs"""
total = db.query(BlogPost).count()
blogs = (
db.query(BlogPost)
.order_by(BlogPost.created_at.desc())
.offset(skip)
.limit(limit)
.all()
)
return {"total": total, "blogs": blogs}
Key features:
- Automatic request validation via Pydantic
- Database transactions with rollback on error
- Pagination support for blog listing
- Type-safe responses
Frontend Deep Dive
While I’m primarily backend-focused, I intentionally built the frontend myself to understand how AI latency, UX feedback, and state management interact in real user flows.
React Component Architecture
I structured the frontend with three main components:
src/
├── components/
│ ├── BlogForm.jsx # Input form
│ ├── BlogDisplay.jsx # Content display
│ └── BlogHistory.jsx # Blog list
├── services/
│ └── api.js # API client
├── utils/
│ └── helpers.js # Utility functions
└── App.jsx
1. BlogForm Component
The input form with validation and user-friendly controls:
// src/components/BlogForm.jsx
import React, { useState } from 'react';
import { Sparkles, Loader2 } from 'lucide-react';
const BlogForm = ({ onGenerate, isGenerating }) => {
const [formData, setFormData] = useState({
topic: '',
tone: 'professional',
length: 'medium',
keywords: '',
});
const [errors, setErrors] = useState({});
const toneOptions = [
{ value: 'professional', label: 'Professional', emoji: '💼' },
{ value: 'casual', label: 'Casual', emoji: '😊' },
{ value: 'technical', label: 'Technical', emoji: '🔧' },
{ value: 'educational', label: 'Educational', emoji: '📚' },
];
const validate = () => {
const newErrors = {};
if (!formData.topic.trim()) {
newErrors.topic = 'Topic is required';
} else if (formData.topic.length < 5) {
newErrors.topic = 'Topic must be at least 5 characters';
}
setErrors(newErrors);
return Object.keys(newErrors).length === 0;
};
const handleSubmit = (e) => {
e.preventDefault();
if (validate()) {
onGenerate(formData);
}
};
return (
Generate Blog Post
{/* Topic Input */}
Blog Topic *
<input
type="text"
value={formData.topic}
onChange={(e) => setFormData({
...formData,
topic: e.target.value
})}
placeholder="e.g., The Future of AI"
className="input-field"
disabled={isGenerating}
/>
{errors.topic && (
{errors.topic}
)}
{/* Tone Selection */}
Writing Tone
{toneOptions.map((option) => (
<button
key={option.value}
type="button"
onClick={() => setFormData({
...formData,
tone: option.value
})}
className={`p-3 rounded-lg border-2 ${
formData.tone === option.value
? 'border-primary-500 bg-primary-50'
: 'border-gray-200'
}`}
>
{option.emoji}
{option.label}
))}
{/* Submit Button */}
{isGenerating ? (
<>
Generating Content...
</>
) : (
<>
Generate Blog Post
</>
)}
);
};
export default BlogForm;
UI Features:
- Real-time validation
- Visual feedback on selection
- Loading states during generation
- Disabled state management
- Clean, modern design with Tailwind
2. API Integration
Clean API client using Axios:
// src/services/api.js
import axios from 'axios';
const API_BASE_URL = import.meta.env.VITE_API_URL || 'http://localhost:8000';
const api = axios.create({
baseURL: `${API_BASE_URL}/api/v1`,
headers: {
'Content-Type': 'application/json',
},
timeout: 120000, // 2 minutes for AI generation
});
export const blogAPI = {
generateBlog: async (data) => {
const response = await api.post('/generate', data);
return response.data;
},
getAllBlogs: async (skip = 0, limit = 20) => {
const response = await api.get(`/blogs?skip=${skip}&limit=${limit}`);
return response.data;
},
getBlog: async (id) => {
const response = await api.get(`/blogs/${id}`);
return response.data;
},
deleteBlog: async (id) => {
const response = await api.delete(`/blogs/${id}`);
return response.data;
},
};
Why this structure:
- Centralized API configuration
- Easy to add interceptors later
- Clean function signatures
- Environment-based URL configuration
3. Utility Functions
Helper functions for common tasks:
// src/utils/helpers.js
// Copy to clipboard
export const copyToClipboard = async (text) => {
try {
await navigator.clipboard.writeText(text);
return true;
} catch (err) {
console.error('Failed to copy:', err);
return false;
}
};
// Download as file
export const downloadAsFile = (content, filename, type = 'text/markdown') => {
const blob = new Blob([content], { type });
const url = URL.createObjectURL(blob);
const link = document.createElement('a');
link.href = url;
link.download = filename;
document.body.appendChild(link);
link.click();
document.body.removeChild(link);
URL.revokeObjectURL(url);
};
// Get SEO score color
export const getSEOScoreColor = (score) => {
if (score >= 80) return 'text-green-600 bg-green-100';
if (score >= 60) return 'text-yellow-600 bg-yellow-100';
return 'text-red-600 bg-red-100';
};
// Format date
export const formatDate = (dateString) => {
return new Date(dateString).toLocaleDateString('en-US', {
year: 'numeric',
month: 'long',
day: 'numeric',
hour: '2-digit',
minute: '2-digit'
});
};
Challenges & Solutions
Challenge 1: AI Response Consistency
Problem: The AI sometimes generated content in inconsistent formats, making it hard to extract titles and structure.
Solution: I built a robust post-processing pipeline:
- Multiple fallback strategies for title extraction
- Regex-based cleaning for artifacts
- Format normalization across different outputs
- Validation before saving to database
# Fallback chain for title extraction
if line.startswith('# '):
title = line.replace('# ', '')
elif first_line and len(first_line) < 100:
title = first_line
else:
title = f"{topic}: A Comprehensive Guide"
Challenge 2: Long API Response Times
Problem: AI generation takes 20-60 seconds, which feels like forever without feedback.
Solution: Multiple UX improvements:
- Clear loading indicators with spinner
- Button state changes during generation
- Success/error notifications
- Smooth transitions when content appears
{isGenerating ? (
<>
Generating Content...
</>
) : (
<>
Generate Blog Post
</>
)}
Challenge 3: Model 503 Errors
Problem: Hugging Face models go to sleep after inactivity, returning 503 errors on first request.
Solution: Implemented retry logic with exponential backoff:
if response.status_code == 503:
print("Model loading... waiting 30 seconds")
time.sleep(30)
continue # Retry
This handles the warm-up period gracefully.
Challenge 4: SEO Score Fairness
Problem: Creating a fair, comprehensive scoring algorithm that doesn't over-penalize or over-reward.
Solution: Multi-factor analysis with balanced weights:
- 25 points for word count (most important)
- 25 points for keyword usage
- 20 points for structure
- 15 points for title optimization
- 15 points for content completeness
Each factor is independently scored and summed to 100.
Challenge 5: Database Schema Design
Problem: How to store flexible keyword lists and maintain efficient queries?
Solution:
- Keywords as TEXT field with comma separation
- Indexed fields (
topic,id) for fast lookups - Separate
titleandcontentfields for granular access - Timestamps for sorting and filtering
Current Status & Demo
Here's what we have working right now:
Backend:
- FastAPI server running smoothly
- Database models and migrations
- AI integration with Hugging Face
- SEO scoring algorithm
- Complete CRUD API
Frontend:
- Beautiful, responsive UI
- Blog generation form
- Real-time content display
- Blog history management
- Copy and download features
Features:
- Multiple writing tones
- Flexible content length
- Keyword optimization
- SEO scoring
- Content persistence
Try it yourself:
# Clone the repo
git clone https://github.com/yourusername/ai-blog-generator
# Backend
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reload
# Frontend
cd frontend
npm install
npm run dev
What I Learned
Technical Lessons
- Prompt Engineering Matters: The quality of AI output is 80% about the prompt. I spent hours refining the structure.
- Post-Processing is Essential: Never trust raw AI output - always clean, validate, and structure it.
- UX During Long Operations: Loading states, progress indicators, and clear feedback are crucial for good UX.
- Type Safety Saves Time: Pydantic validation caught countless bugs before they reached production.
- Database Indexing: Proper indexing on topic and timestamps made queries 10x faster.
Soft Skills
- Breaking Down Complexity: Dividing the project into services made it manageable.
- Iterative Development: Started with basic generation, then added SEO, then optimized prompts.
- User-First Design: Always thinking "how would I want this to work?" improved the UX significantly.
🚀 What's Next (Part 2)
In the next post, I'll cover:
- Deploying Backend to Railway
- Setting up PostgreSQL
- Environment variables
- Handling production errors
Deploying Frontend to Vercel
Build optimization
Environment configuration
Custom domain setup
Production Optimizations
Caching strategies
Rate limiting
Monitoring and logging
Future Enhancements
Image generation for blog posts
Multi-language support
Content scheduling
Analytics dashboard
Key Takeaways
If you're building an AI-powered application, here are my top recommendations:
- Start Simple: Get basic generation working before adding complexity
- Invest in Prompt Engineering: It's the most important factor for output quality
- Build Robust Error Handling: AI APIs fail - plan for it
- Focus on UX: Long wait times need great feedback
- Test with Real Users: Get feedback early and iterate
### Resources & Links
- GitHub Repository
- Live Demo: Coming in Part 2!
- FastAPI Docs
- Hugging Face
- Tailwind CSS
Let's Connect
Building this project was an incredible learning experience. I'd love to hear your thoughts:
- What features would you add?
- What challenges have you faced with AI integration?
- Any questions about the implementation?
Drop a comment below or connect with me:
📢 ### Stay Tuned for Part 2!
Next up: Deployment to Production
I'll walk through:
- Railway deployment for backend
- Vercel deployment for frontend
- Domain setup and SSL
- Production monitoring
- Performance optimization
Make sure to follow me to get notified when Part 2 drops!
Thanks for reading! If you found this helpful, please give it a ❤️ and share with others who might benefit from it.
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