AI-Powered Blog Post Feedback Pipeline

[ShpendKe]

Goal

Get automatic feedback on blog posts from different reader perspectives before publishing.

How It Works

1. Write blog post with tags (e.g., cloud, devops, security)
2. Pipeline reads tags and selects matching personas
3. Each persona reviews the article and gives:
   • Rating: 1-10
   • What's good
   • What's missing
4. Get aggregated feedback report

Personas

1. Cloud Architect

Tags: cloud, aws, kubernetes, architecture

Example Feedback:

Score: 7/10
✅ Good: Clear architecture diagram
❌ Missing: Cost estimates, scaling limits

2. Junior Developer

Tags: tutorial, basics, getting-started

Example Feedback:

Score: 5/10
✅ Good: Code examples included
❌ Missing: Prerequisites unclear, too much jargon

3. DevOps Engineer

Tags: devops, ci-cd, docker, terraform

Example Feedback:

Score: 8/10
✅ Good: Practical CI/CD example
❌ Missing: Monitoring setup, rollback strategy

4. Security Engineer

Tags: security, authentication, compliance

Example Feedback:

Score: 4/10
✅ Good: HTTPS mentioned
❌ Missing: Secrets hardcoded!, no auth explained

Example: How It Works

Your blog post:

---
title: "Kubernetes Multi-Cluster Setup"
tags: [cloud, kubernetes, devops]
---

Pipeline selects:

• ✅ Cloud Architect (matches: cloud, kubernetes)
• ✅ DevOps Engineer (matches: devops, kubernetes)
• ❌ Junior Developer (no match)
• ❌ Security Engineer (no match)

Feedback Report Example

# Feedback: "Kubernetes Multi-Cluster Setup"

Overall Score: 7.5/10

## Cloud Architect: 8/10
✅ Good architecture
❌ Missing: cost analysis

## DevOps Engineer: 7/10  
✅ Good examples
❌ Missing: monitoring

## Top Issues to Fix:
1. Add monitoring setup
2. Include cost estimates
3. Add troubleshooting section

Tasks

• Set up GitHub Actions workflow
• Create persona prompt templates
• Implement tag matching logic?
• Generate feedback reports
• Post as PR comments