Prompt engineering – How to optimize context in code generation prompts?

Prompt engineering involves the strategic crafting of inputs to large language models to produce specific outputs, with the quality of the output being heavily influenced by the input provided. A technique to optimize prompt token limits involves using classical optimization algorithms like the greedy 0/1 knapsack algorithm, which is applied to maximize the value of code context tokens included in a prompt within a specified limit. In this context, tokens are selected based on their contribution to the quality of generated code, and the algorithm iteratively chooses the most valuable tokens until the token limit is reached. While the greedy 0/1 knapsack algorithm may not always provide the optimal solution, its efficiency and simplicity make it useful for optimizing prompt tokens in code generation scenarios. Limitations include its inability to account for constraints between non-fractional items, but it remains an effective tool for achieving high-quality code generation when the token values are carefully selected.