Guides & Strategies6 min read•Updated July 2026
Compression Strategies Guide
Choosing the optimal strategy for System Prompts, RAG Contexts, and Chat Logs.
Strategy 1: Query-Aware RAG Context Compression
When compressing retrieved vector documents, prioritize content highly relevant to the user query:
rag_strategy.py
from llmslim import compress_documents
retrieved_docs = ["Chunk 1 text...", "Chunk 2 text...", "Chunk 3 text..."]
user_query = "What is the Q3 net margin for enterprise hardware?"
# Filter & compress RAG contexts by query relevance score
compressed_chunks = compress_documents(
retrieved_docs,
query=user_query,
target_ratio=0.3
)
final_prompt = "\n---\n".join([c.compressed_text for c in compressed_chunks])Frequently Asked Questions
Which strategy should I use for 50-page PDF RAG contexts?
Use RAG Document Chunk strategy with compress_documents(docs, query='...') to rank sentences by query relevance.