Enable AI assistant to reference company best practices and policies in every chat

Challenge

Product teams and organizations want to leverage AI assistants that deliver reliable, organization-specific answers based on their own curated internal documents and guidelines. Currently, there is no effective way for teams to create, manage, and link a dedicated information repository in Aha! to the AI assistant for it to reference when responding. This limits the assistant usefulness and makes it challenging to ensure it is providing accurate information in all responses.

Impact

Without the ability to connect a repository of company information and documents, the AI assistant provides incomplete or generic answers, misses organizational context, and fails to reflect best practices or proprietary processes. This limits trust, adoption, and the value of AI for product teams, stakeholders, and customers.

Proposed solution

An organization would create a Knowledge base in Aha! containing their policies, checklists, guidelines, and best practices for creating products.

Then allow them to link this Aha! KB to their AI assistant at the workspace line or account level, so that all answers are grounded in organization-approved content.

All agents and chats with the AI assistant will now have access to this foundational information and can use it to provide more accurate guidance to users.

Examples:

• A product team links a "Go-to-Market Checklist" and "Brand Voice Guide" to ensure AI reviews marketing briefs against their real standards.

• A product team links their "Product Requirements Document (PRD) Template" and "Feature Prioritization Framework" so the AI can review new feature ideas or specs for completeness and alignment with established evaluation criteria.

• A team connects their "Release Planning Checklist" and "Roadmap Communication Guidelines" to enable the AI to help PMs ensure every release plan meets internal standards and stakeholder communication best practices.

From a technical perspective, this is an AI implementation pattern called Retrieval-Augmented Generation (RAG). The key is that the AI isn't just "choosing" to search the KB from a list of tools. Instead, the system is fundamentally designed to use the information in the KB to inform its answers.

With this technique, it can generate an accurate, helpful response that are grounded in the organization's actual best practices.