Most prompt compression discussions focus on the happy path: you have a long RAG context, you trim it to 50% of tokens, and your API bill halves. What rarely gets discussed are the failure modes: dropped system instructions, truncated JSON schemas, broken code fences, and entity names that get quietly pruned because they scored low in some similarity metric. LLMSlim (https://www.llmslim.app) is a Python library I've spent the last few months building to handle these edge cases properly.
