Optimize before embedding
The cheapest chunk is the one you never create. Boilerplate removal and structure preservation should happen before embeddings, not after retrieval disappoints.
Technical blogTechnical article
How to reduce token costs for RAG
RAG cost is heavily shaped by input quality. If your ingestion layer passes noisy HTML forward, you pay more to embed, store, retrieve and prompt content that never helps answer the question.
Primary use
A technical guide to lowering RAG spend by cleaning documents before chunking and embedding.
Recommended flow
Fetch, clean, measure tokens, then hand consistent Markdown to agents or retrieval systems.
Next step
Use the Playground to compare raw HTML against optimized output before integrating the API.
Measure token changes deterministically
Use one tokenizer for before and after metrics so teams can set budgets and watch regressions over time. Heuristic size estimates are not enough in production.
Treat repeated chrome as technical debt
Repeated navigation and marketing modules multiply across crawls. Removing them once in the ingestion layer produces compounding savings across indexing and prompt usage.
Internal links
Related technical paths
Token efficiency
Context optimization for LLM retrieval
Reduce noisy HTML and preserve semantic structure so every prompt and chunk carries more useful signal per token.
Chunk quality
Semantic chunking starts with clean structure
Chunk by meaning instead of arbitrary token windows by preserving headings, lists and tables before segmentation.
Cost control
Reduce HTML token usage before RAG or agents
Shrink token spend by converting bloated HTML into compact Markdown before chunking, prompting or embedding.