RAG is complicated. Vector databases, chunking strategies, and retrieval failures. Sometimes, you just want to put the whole document in the context window. Now you can.
Start Optimizing for FreeYou chunk your documents, embed them, store them in Pinecone or Qdrant, and use semantic search to fetch relevant snippets. The flaw? Semantic search often misses cross-document context, and setting up the infrastructure takes weeks of engineering time.
Instead of building a brittle vector database, just convert your entire dataset into a few dense PNGs using PXINK. Feed all the images directly into Claude Sonnet 4.6's 200K context window. The model sees everything at once, and the visual compression makes it 88% cheaper.
If you have gigabytes of data (like Wikipedia or a 10-year internal wiki), you still need RAG. The context window isn't big enough for that. But if you have 1-5 megabytes of text, visual encoding is faster, cheaper, and yields better reasoning.
A single 1000x1000 pixel image generated by PXINK can comfortably hold over 25,000 characters of text, costing a fraction of a cent to process in Claude.