5 Mistakes Killing Your AI App (And How to Fix Them)

Stop building AI apps the way tutorials teach you. Most of them are dead on arrival. I've seen dozens of AI projects fail — not because the model was bad, but because developers made the same 5 mistakes over and over. Here's what they are and how to avoid them. Mistake #1: Treating the LLM Like a Database The number one mistake I see: prompt = f " Based on this data: { entire_database_dump } , answer: { question } " You're shoving everything into the prompt and hoping the model figures it out. This fails because: Context windows have limits. Even with 128k tokens, you'll hit them fast with real data. More context = worse performance. Models get confused with irrelevant information (the "needle in a haystack" problem). It's expensive. You're paying per token. Sending 50k tokens when you need 500 is burning money. Fix: Use RAG (Retrieval Augmented Generation). Embed your data, search for relevant chunks, and only send what matters. # Bad response = llm ( f " Here ' s 10000 rows of data: