How to build search that finds the right product or record

Collect the searches people actually make

A search box is useful when people can find the information they need and recognize the correct result. Poor results can leave customers browsing elsewhere and staff asking colleagues to find records the system already holds.

Search implementation should begin with the user's question, the available information and the action that follows a result. The selected search product is part of that design.

Read the source: Elastic: Search with Elasticsearch.

Collect representative searches

Bring examples from real tasks, including common wording, incomplete information and queries that currently fail. Record what a useful result would be and why. Keep a separate set of examples for acceptance testing.

For ecommerce search implementation, include product names, attributes, availability and the difference between an exact item and a suitable alternative. Decide how commercial priorities interact with relevance so the result remains useful to the customer.

Check the underlying records

Identify which system owns the searchable information and how updates reach the search service. Include corrections, deleted records and changes to user access. A fast index can still present outdated or unauthorized material.

Elasticsearch consulting should identify the data preparation, indexing, query and ranking work included. Elastic’s documentation describes how those parts work together in a search application.

Choose the matching approach

Semantic search development should be tested on the meaning users intend, including requests that do not use the exact words in a record. Compare it with keyword search and any combined approach on the same examples.

Vector database setup is one possible component of similarity-based search. Ask what information will be represented, how new or changed records are handled and how permissions apply when a result is retrieved. Include filters, permissions and how search results are presented in the same acceptance test.

Compare implementation proposals through the task

OpenSearch consulting or Algolia implementation should be assessed against the same search examples, access requirements and update needs. Confirm the current product features, plan limits and operating costs relevant to the proposed design.

Ask the supplier to explain filters, sorting, empty results and the route for reporting a poor answer. Give each supplier the same records and a set of examples they did not choose.

Test relevance and freshness together

For an illustrative acceptance exercise, add a new item, change its availability and remove another record. Have users search with both exact and everyday wording. Check that appropriate results appear and changes reach the index within the agreed window.

Include a user who should not see a particular record. A result title or preview can reveal information even if opening the full record is blocked, so review the complete presentation.

Keep improvement measurable

Track unsuccessful searches, useful result selections and completed tasks where measurement is appropriate. Review recurring queries and update the acceptance set as the catalogue or business information changes.

AI App Studio can help build a search experience around the records and decisions your users need. Book a call with examples of searches that currently fail.