IDInternals Decodedininternals-decoded.hashnode.dev·6h ago · 12 min readStructured Outputs: JSON You Can Actually ParseWhen your customer-support reply assistant extracts a customer's name, order number, and issue category from a chat transcript, you need that data to be machine-readable every single time. Structured 00
IDInternals Decodedininternals-decoded.hashnode.dev·2d ago · 12 min readChain of Thought: When Thinking Out Loud HelpsChain-of-thought prompting tells a language model to generate explicit intermediate reasoning steps before the final answer. Internally, this aligns inference with the training data’s step-by-step exp00
IDInternals Decodedininternals-decoded.hashnode.dev·4d ago · 11 min readFew-Shot: Examples Beat InstructionsLast time we built the skeleton of a prompt that behaves: role, task, constraints, and format. That skeleton gives you structure. Now we add the muscle. A few well chosen examples will steer the model00
IDInternals Decodedininternals-decoded.hashnode.dev·6d ago · 12 min readWhy Your Prompts Fail (and the Anatomy That Works)Most prompts fail not because you forgot a magic phrase but because you are driving a probabilistic sequence model with a brittle text interface. The prompt becomes tokens, gets embedded, passes throu00
IDInternals Decodedininternals-decoded.hashnode.dev·Sep 4 · 12 min readHow to Actually Choose a ModelChoosing a model is a multi-objective decision. You weigh accuracy, latency, cost, context length, and safety against the hard constraints of your specific task. No single benchmark or leaderboard ran04A
