Context waste
Long prompts and noisy retrieved chunks push up cost and latency.
Use case
Optimize knowledge-base RAG by selecting trusted chunks, preserving citations and preventing excessive context.
Long prompts and noisy retrieved chunks push up cost and latency.
Repeated failures multiply spend without improving the outcome.
Simple requests are often sent to models that are more expensive than needed.
Blind savings can remove important facts, dates or source constraints.
Next step
Share a lightweight workload profile and ML Mind will map your likely waste sources, starting deployment level and safe savings opportunities.