---
title: "reranking | Skills We Assign For | Azendo"
description: "Reranking in retrieval pipelines — cross-encoders, cost and latency, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/reranking/"
---

[Skills](https://azendo.co/skills/) AI and machine learning 

# reranking.

Reranking reorders an initial set of retrieved candidates using a more expensive model that scores query and document together, rather than comparing independent embeddings. It improves precision at the top of the list.

## Where reranking fits on a long engagement.

The two-stage pattern exists because the accurate method is too slow to run over everything. A vector search retrieves fifty plausible candidates cheaply, then a cross-encoder — which processes query and document together and is therefore far more accurate — reorders those fifty. Running the cross-encoder over the whole corpus would be prohibitive.

The improvement is usually substantial, and it is concentrated exactly where it matters. Relevance in the top three results is what a retrieval-augmented system actually depends on, since that is what fits in the context, and reranking is the most reliable way to improve it.

## What an assigned team does with reranking.

It costs latency and compute on every query. Whether that is acceptable depends on the interaction: a research tool can absorb a few hundred milliseconds, a typeahead cannot.

Making that trade explicitly, with numbers, is part of designing a retrieval system properly under [ai engineering services](https://azendo.co/services/ai-engineering/).

## What we use reranking for.

* Precision where the context window ends Better top-three results, which is what the model actually receives.
* A cheap first stage and an accurate second Broad retrieval followed by expensive scoring over a small candidate set.
* Latency budget decided explicitly The added time measured against what the interaction can absorb.

## How reranking capacity is assigned.

Retrieval quality work is assigned inside AI capacity, with latency cost quantified rather than accepted implicitly.

## Roles we assign reranking for

* [LLM Engineer AI engineering](https://azendo.co/services/ai-engineering/llm-engineer/)

## Service lines it sits in

* [AI engineering](https://azendo.co/services/ai-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in ai and machine learning

* [TensorFlow — skill we assign for](https://azendo.co/skills/tensorflow/)
* [scikit-learn — skill we assign for](https://azendo.co/skills/scikit-learn/)
* [Pandas — skill we assign for](https://azendo.co/skills/pandas/)
* [NumPy — skill we assign for](https://azendo.co/skills/numpy/)
* [embeddings — skill we assign for](https://azendo.co/skills/embeddings/)
* [function calling — skill we assign for](https://azendo.co/skills/function-calling/)
* [vLLM — skill we assign for](https://azendo.co/skills/vllm/)
* [MLflow — skill we assign for](https://azendo.co/skills/mlflow/)

## Tell us what your roadmap needs reranking for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)