Coveo Cloud Project Guide
- Understanding the Coveo Relevance Maturity Model
- Search Project Overview
- Search Project Management
- Initial Configuration
- Locating and Indexing Content
- Designing the Search Experience
- Tuning Relevance
- Common Pitfalls
- Evaluate Your Implementation Before Going Live
The Coveo Platform offers powerful tools to help you continuously improve relevance in your search solution.
Most end-user interactions with your search experience record usage analytics data in your Coveo organization. By regularly inspecting dashboards and reports based on this data, you can identify actual relevance issues in your search solution. You can then configure query pipelines to conditionally apply ranking expressions, thesaurus rules, filters, and other manual relevance tuning features in order to deal with those issues.
Once you have gathered sufficient usage analytics data and your search solution is yielding satisfyingly relevant results, you can generate Coveo Machine Learning (Coveo ML) models to start providing your end users with increasingly relevant and personalized interactions.
This section focuses of the relevance tuning phase of your search project.
This is one of the most important sections of this guide. Previous sections should be considered prerequisites; tuning relevance on top of a messy index or badly designed search experience will likely have little impact.
Articles in this section
This section is a work in progress; more articles will be progressively added.