My AI chatbot was hallucinating wild answers to customers. Here's how I implemented RAG (Retrieval-Augmented Generation) to fix it, covering Vector DBs, Embeddings, and Hybrid Search.
Both are children of Transformer, so why the difference? Using 'Fill-in-the-blank' vs 'Write-next-word' analogies to explain BERT vs GPT. Practical guide based on trial and error.
ChatGPT answers questions. AI Agents plan, use tools, and complete tasks autonomously. Understanding this difference changes how you build with AI.
I actually used all three AI coding tools for real projects. Here's an honest comparison of Copilot, Claude Code, and Cursor.
I share my experience with overfitting in machine learning. I was fooled by 99% training accuracy, only to fail in production. Learn how I used Dropout, Regularization, and Data Augmentation to build 'real intelligence' instead of a memorization machine.
After implementing RAG, hallucinations dropped by 99%. Now the chatbot knows how to say, "Sorry, that's not in the manual," which is infinitely better than a confident lie.
If you're building an AI service, remember: It's much more cost-effective and reliable to feed the AI the right context (RAG) than to try and make the model itself smarter (Fine-tuning).
Our goal isn't to build a 'Genius AI', but a 'Trustworthy Service'.
