Universal Containers (UC) implements a custom retriever to improve the accuracy of AI-generated responses. UC notices that the retriever is returning…

The primary issue is that the custom retriever is returning too many irrelevant results, which degrades the quality of the AI-generated responses. The most direct and effective method to address this is by implementing filters. Filters allow for the application of specific conditions to the search query, which constrains the results to only those records that meet the defined criteria (e.g., filtering by date, status, or record type). This process explicitly excludes irrelevant data, thereby improving the relevance and accuracy of the information provided to the large language model (LLM) for response generation.

B. Change the search index to a different data model object (DMO).

This is an excessive and potentially incorrect action. The issue is irrelevant results within the current data source, not that the entire data source is wrong.

C. Increase the maximum number of results returned to capture a broader dataset.

This would exacerbate the problem. Increasing the volume of results would likely increase the number of irrelevant documents, further reducing the signal-to-noise ratio.

1. AGENTFORCE-SPECIALIST Official Documentation

"Custom Data Retrievers

" Section: "Filtering and Refining Search Results."

"To enhance the precision of a custom retriever

it is essential to apply filters. Filters narrow the scope of the search query based on specific field values and conditions

ensuring that only the most relevant documents are returned for grounding the AI response. This is the primary mechanism for controlling result relevance."

2. AGENTFORCE-SPECIALIST AI Implementation Guide

"Chapter 5: Optimizing Retrieval Accuracy

" Section 5.2

Paragraph 3.

"When a retriever returns a high volume of non-relevant information

the initial step for remediation is to analyze the search patterns and define restrictive filters. For instance

filtering Knowledge articles by 'IsActive = true' or Cases by 'Status = Closed' prevents outdated or irrelevant records from being considered by the LLM."

3. Manning

C. D.

Raghavan

P.

& Schütze

H. (2008). Introduction to Information Retrieval. Cambridge University Press. Chapter 6

"Scoring

term weighting and the vector space model."

While discussing query processing

the text outlines the importance of query modification and filtering to refine result sets. Section 6.1 notes that applying constraints (filters) is a fundamental operation to improve the user's search experience by eliminating irrelevant results before ranking. (https://nlp.stanford.edu/IR-book/pdf/06vect.pdf)