LLMs Don’t Break Loudly — They Drift Silently: Why Prompt Validation Is the Missing Guardrail in AI Systems
A product manager asked: “Can you summarize the return policy for electronics?” The assistant replied confidently: “Returns are allowed within 7 days if the product is unopened.” Except the policy said 14 days.
Same Input, Different Output: How LLM Drift Silently Broke Our Production Pipeline
A user asked our assistant: “List all customers who placed orders in the last 30 days.” The backend used GPT to generate the SQL: SELECT * FROM orders WHERE order_date >= '2024-04-01'; It worked. The next day, the same prompt returned: SELECT * FROM orders WHERE order_date >= '2024-04-01' AND status = 'shipped'; No warning. No error. Just a new condition the user never asked for.
The Unsung Hero - How Good Feature Engineering Outperforms Deep Models in Many Real-World Cases
In a world obsessed with deep learning and massive models, it’s easy to forget that sometimes, the real magic lies in the data — or more precisely, in how you shape it. You don’t always need a transformer or a GPU cluster to win. Many times, all you need is feature engineering done right.
What Recurrent Neural Networks Are Really Doing: A Simple Intuition
When you watch the stock market, you don’t just look at today’s price in isolation. You carry a memory: “The stock has been rising for the last few days,” or “The trend looks like it’s turning downward.”
Clarity Is Not Simplicity: How Real Work Demands Sharp Thought
When a technical report says: “We trained a neural network with 90% accuracy on the dataset.” it feels simple, clean, and reassuring. But when the report says: “We trained a feedforward neural network with