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.
Three core patterns that show up repeatedly in AI agent systems—Tool Use, ReAct, and Chain of Thought—explained with real TypeScript code. Understanding these patterns makes agent design significantly clearer.
ChatGPT answers questions. AI Agents plan, use tools, and complete tasks autonomously. Understanding this difference changes how you build with AI.
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.
I actually used all three AI coding tools for real projects. Here's an honest comparison of Copilot, Claude Code, and Cursor.
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'.
