Yashvardhan Thanvi

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.

I run a lot of RAG pipelines. And for a while, I was doing what most people do: retrieve the top-k documents, concatenate them into the context, and ship the whole thing to the LLM. It worked. But when I started looking closely at what was actually in those contexts, I noticed something uncomfortable: a huge fraction of every prompt was filler. Background sentences that restated the obvious. Transition paragraphs that connected ideas the model didn't need connected.