Steering rerankers with natural language instructions
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Building AI agents? The challenge isn't prompt engineering, it's context engineering. Voyage AI’s instruction-following rerankers can prioritize what lands in your LLM's context via natural language instructions.
📝 With instruction-following rerankers, you can dynamically steer the reranking process with natural language instructions. Specify what matters—source hierarchy, application-specific rules—and the reranker adjusts relevance scores accordingly.
Learn more in our blog: https://t.co/ps6uPjhHG0
Context
Voyage AI's instruction-following rerankers let developers specify what should count as relevant before information reaches an answering model. A search first gathers candidate documents; the reranker then scores and reorders them. Adding plain-language instructions can change that ordering to favor a particular source category or kind of content. The company's technical explanation shows how this works with rerank-2.5.
In one example, a troubleshooting query comes with an instruction to prioritize workarounds and step-by-step debugging, and documents of that kind move toward the top. The query can stay the same while the ranking reflects what the user needs to do. That moves part of the control over an answer into selecting its supporting material.
- context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
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