The core issue is the retrieval of outdated documents by the AI. A custom retriever provides the necessary control to filter the data source before it is passed to the Large Language Model (LLM). By configuring a custom retriever with a filter condition—such as a SOQL WHERE clause on the LastModifiedDate field—Universal Containers can programmatically instruct the system to only fetch documents that have been updated within a specific recent timeframe (e.g., the last 180 days). This directly and reliably solves the problem by ensuring the context provided to the AI is always current, thus improving the relevancy of its responses.

A. Switching to a Knowledge base is a good data management practice, but it does not automatically manage recency for AI retrieval. A filter would still be required to ensure only current articles are retrieved.

C. This passive approach is unreliable and inefficient. It does not prevent the AI from retrieving existing outdated documents and fails to provide a deterministic solution to the user-reported problem.

1. Official Vendor Documentation (Salesforce): Einstein Copilot > Custom Actions > Apex Retriever for Custom Data. The documentation details how to create custom Apex retrievers to fetch specific data for the AI. It explicitly shows that developers can write custom SOQL queries within the Apex class. This allows for precise filtering

such as WHERE LastModifiedDate = LASTNDAYS:90

which directly supports the solution described in option B. (Reference: Salesforce Einstein 1 Platform Developer Guide

Section: "Implement a Custom Retriever with Apex").

2. Official Vendor Documentation (Salesforce): Prompt Builder > Grounding with Salesforce Data > Data Source Filtering. When setting up data sources for RAG

advanced filtering is achieved through customization of the retrieval mechanism. The default retriever performs a broad semantic search

but for criteria-based selection like recency

a custom implementation is the designated solution. (Reference: Salesforce Help & Training

Article: "Ground Your Prompts with Relevant Salesforce Data").

3. Academic Publication (General RAG Principle): Lewis

P.

et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In Advances in Neural Information Processing Systems 33 (NeurIPS 2020). While not specific to AGENTFORCE-SPECIALIST

this foundational paper on RAG discusses the retriever component's critical role. The principle of refining the retriever to fetch more relevant documents—which includes filtering by metadata like dates—is a core concept for improving RAG system performance. Section 2.1

"Retriever

" outlines the mechanism that can be customized. DOI: https://doi.org/10.48550/arXiv.2005.11401