Unusual Traffic, Unexpected Chaos: The Truth Behind the Cloudflare Outage

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Cloudflare's Global Outage: Understanding the Root Cause Behind the September 18 Network Disruption On 18 September, the world witnessed a sudden and widespread slowdown of the internet. Websites and applications depending on Cloudflare ranging from social networks to API driven platforms began returning Internal Server Error messages. Millions of users assumed it was a cyberattack, a DDoS event, or a breach. However, the truth behind the outage was far more nuanced and rooted in Cloudflare’s internal architecture. This blog breaks down what Cloudflare does, why bot mitigation plays a critical role, how an unexpected configuration file led to an internal service crash, and why “unusual traffic” triggered a global ripple. What Cloudflare Really Does for the Internet Cloudflare is more than a simple CDN or firewall it is a massive reverse-proxy network that sits between users and websites. It accelerates content delivery, filters malicious traffic, provides DNS services, manage...
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Retrieval Augmented Generation

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Mastering RAG: The Future of Fact-Aware AI 

“An AI that thinks like L, but researches like Sherlock.”
– That’s the power of RAG.

What is RAG?

RAG stands for Retrieval-Augmented Generation — an advanced AI technique that combines a retriever (like a search engine) with a generator (like GPT).

Traditional AI only replies based on training. RAG goes further — it retrieves live facts from external documents before generating an answer.

Why RAG Was Introduced

Most LLMs are like students who read thousands of books last year — but can’t learn new things today. RAG fixes this by:

  • Reducing hallucinations (wrong facts)
  • Adding real-time knowledge to AI responses
  • Combining search + generation into one smart process

What RAG Replaces

Before RAG, you needed to:

  • Manually feed facts into prompts
  • Use Google + ChatGPT separately
  • Retrain your models constantly

RAG solves this with one pipeline that searches, reads, and generates together.

RAG Workflow: Step by Step

Let’s say you ask: “What are the risks of cloud storage?”

  1. Input: Your question is received
  2. Embedding: It is converted into numbers that represent meaning
  3. Search: Those numbers are used to search a vector database
  4. Retrieve: The most relevant documents are selected
  5. Generate: The AI reads those and creates a meaningful answer


Key Concepts Made Simple

🔹 Text Embeddings

A way to convert sentences into numbers. Similar meanings get similar numbers. It’s like mapping ideas into a digital space.

🔹 Vector Database

A searchable storage that uses embeddings. It finds the most relevant documents by meaning, not just keywords.

Popular tools: FAISS, Pinecone, Chroma

🔹 Retriever + Generator

The retriever finds the right data. The generator uses it to write a human-like answer. Combined, they become a super-smart assistant.


Where RAG Shines

  • Company-specific AI assistants
  • Chatbots that answer using your documents
  • Research tools with up-to-date knowledge
  • Educational tutors powered by personal notes

Final Thoughts

RAG is the future of intelligent AI. It combines knowledge, research, and writing into one smooth process.

If you're building next-gen AI, don’t just use memory — teach it to read and respond like RAG does.

🔗 Stay Tuned!
Next, I’ll post a full code walkthrough of RAG using LangChain + FAISS + GPT.
Let’s keep learning like ( Death Note ) L and deducing like Sherlock! 

Location: Kanchipuram, Tamil Nadu 631502, India

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