Nature of the problem
Large language models can produce code that seems reasonable but is wrong on complex algorithmic problems, especially when boundary conditions and mathematical derivations are involved.
Systematic solutions
- Triple Verification Mechanism::
- Test case validation built into the tool (needs to be turned on in settings)
- Comparison of sample output with platforms such as LeetCode
- Cross validation using different API service providers
- Prompt Engineering Optimization: Add in custom tips
"请逐步解释并验证每个步骤的正确性"request - confidence level (math.): Enabled in advanced settingsAnswer Credibility ScoreFunction (based on logprob values returned by the model)
workflow
When you find a suspicious answer: 1) Run a quick validation in the local IDE 2) Use the"In-depth analysis"Patterns require AI to identify potential bugs 3) Compare and contrast at least two different solutions
risk control
It is recommended to focus on the AI givenA framework for solving problemsInstead of copying the code directly, it is especially effective for problems such as complex recursion and dynamic programming. Also maintain the habit of writing core algorithms manually.
This answer comes from the articleCoding Interview Solver Assistant: An AI Solver for Analyzing Programming Interview Questions in Real TimeThe
