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Reasoning-Effort Controls: Match the Model to the Task A request to extract an invoice date and a request to compare several engineering designs ask different things of a model. Reasoning-effort controls help allocate deliberation to the task where the selected model supports them. The useful setting is the one that…

RAG Re-Ranking: Choose Useful Context for Your LLM Retrieval often returns passages about the right topic without identifying the ones that answer the exact question. RAG re-ranking adds a second selection step: score the candidates against the question, then choose what enters the model’s context. This can make a document…

Prompt Compression with LLMLingua-2: Practical Limits A long prompt often contains useful facts mixed with repeated explanation. Prompt compression tries to keep the information the answering model needs in less text. LLMLingua-2 approaches this through token classification: it selects parts of the input to retain. The practical question is not…

Semantic Caching for LLMs: Matching and Safe Scope Two people may ask the same question in different words and still need the same answer. Semantic caching can reuse a suitable stored response in that situation. The important word is suitable: similarity alone does not establish permission, freshness or relevance to…

LLM Cost Optimization: 12 Ways to Reduce Wasted Work LLM cost optimization starts by finding wasted work: repeated requests, unnecessary context, unsuitable model choices or excessive reasoning. Caching, compression and routing address different causes. This article explains twelve techniques and how to evaluate them without assuming a fixed savings percentage.…