context-engineering

To reduce token usage, apply context engineering techniques like Skeleton-of-Thought and diff-style updates. By sending only a function signature and docstring instead of the full file, you minimize tokens sent to the LLM while maintaining reasoning quality.

The lost-in-the-middle problem occurs when LLMs struggle to process information located in the middle of large, unstructured prompts. Context engineering addresses this by using XML tagging and structured context packaging to organize prompts efficiently.

Structure AI prompts using XML tagging, symbolic variables, and Skeleton-of-Thought techniques. These context engineering methods organize information hierarchically, reducing token costs and mitigating the lost-in-the-middle problem for better LLM reasoning.

Use diff-style updates for efficient context management when modifying existing code. Instead of rewriting entire files, requesting targeted diffs significantly reduces token consumption during LLM interactions and lowers overall API costs.

Yes, negative constraints and symbolic variables are practical context engineering techniques. They help bind complex values and explicitly define boundaries for the LLM, directly reducing token waste and improving prompt structure.

LLMBundle-style context packaging is a method to group related prompt elements together. This context engineering technique structures information efficiently, reducing token usage and helping the LLM process instructions without hitting the lost-in-the-middle problem.