prompt-engineering

To fix LLM misclassification, evaluate your prompt against a defined set of test cases to systematically measure accuracy, diagnose failure points, and iteratively refine instructions. This structured evaluation prevents regressions by verifying improvements on real-world inputs.

The best way to evaluate prompt quality is to measure performance against a defined evaluation set of inputs and expected outputs. This systematic testing methodology isolates failure points, tests hypotheses, and verifies accuracy improvements before deploying to production.

Yes, chain-of-thought prompting is a technique applied within the instruction design phase to standardize roles, constraints, and output contracts. This systematic framework ensures reliable model behavior by structuring the reasoning process.

Prompts often fail after model migration due to differences in how new LLMs interpret instructions and constraints. A systematic evaluation framework measures prompt performance against your existing test cases to diagnose failures and iterate on prompt design for the new model.

While few-shot examples help guide behavior, consistent LLM outputs require standardizing instruction design with clear roles, constraints, and output contracts. A structured framework tests whether your few-shot prompts actually improve accuracy across your evaluation set.