Integrating artificial intelligence (AI) features has become a hallmark of innovation in product development. Product managers, often the architects of these advancements, constantly seek ways to enhance their products through AI. However, proving the feasibility of AI features typically involves collaboration with engineers. But what if we told you that as a product manager, you could kickstart the AI journey without relying on engineering support?
This article aims to empower product managers with a comprehensive guide on independently envisioning, structuring, and validating AI features—no engineers required. Following a three-step approach, we’ll explore product entities, structured data extraction, and the magic of AI summarization.
So let’s get started!
Product entities serve as the foundational elements of a product, encompassing components like events, tasks, and notes. Product managers need to determine these entities and their attributes as they generate and validate product concepts. By cataloging these entities and their properties, product managers can extract structured data from textual or visual properties and enhance the entities by incorporating additional properties derived from the existing ones.
Let’s consider a practical example to make this simple. Take, for instance, the “Event” entity in a widely used tool like Google Calendar. This entity encompasses properties such as title, description, and participants. Imagine the wealth of information within these properties.
By answering these questions, you not only identify structured data within text-based properties but also pave the way for enriching these entities further. The properties of an entity act as the raw material, and it’s the product manager’s role to leverage this material to create innovative AI features.
Remember that within the properties of your product entities lies the key to proving that AI features can be built autonomously.
The second step in proving the feasibility of AI features is to structure and enrich the identified product entities. This involves a detailed exploration of each product entity, including extracting structured data from text-based or image-based properties and improving entities with additional properties based on existing ones.
The process of structuring and enriching product entities is fundamental to the successful integration of AI features. It sets the groundwork for leveraging AI to extract valuable insights and enhance the product’s capabilities. By following this approach, product managers can effectively demonstrate the potential of AI integration without immediate engineering involvement.
The strategy of converting product entities into prompts for Language Models (LMs) proves to be a potent tool, harnessing the capabilities of AI to generate insightful summaries. By presenting entities in a textual format and allowing LMs to process and summarize them, product managers gain valuable insights into the potential of AI-driven summarization.
Examples of converting entities to a textual format for summarization include:
These findings highlight the increasing significance of AI in product management and emphasize the necessity for a deliberate approach to its integration.
Implement AI evaluators to mitigate risks to your generative AI app. This way you can seamlessly protect your user experience and brand reputation without needing extensive engineering support.
These risks are inherent to the LLMs, and range from offending or harming your users to offsetting your chatbot’s intended business goal and leaking sensitive information. According to a recent market report over 89% of AI practitioners mentioned that their AI experienced hallucinations. This more than emphasizes the need for more control over AI. Evaluators offers an easy fix to these performance risks and ensures alignment with business KPIs and customer needs.
Another quick tip:
By adhering to these guidelines, product managers can establish a foundation for developing AI features and apps, showcasing the intrinsic value of AI integration within their products.
By following this structure you can develop a concept and put it to proof without depending on engineers. However, this doesn’t mean that engineers are entirely not needed during the process, but it does mean that there is a simpler path for product managers to have more control over AI development.
As mentioned above implementing AI evaluators to keep your LLM-based app’s interaction in check and on point is key in your zero engineer journey. Coralogix AI Evaluators are designed for easy integration – layered between your user interface and RAG/LLM engine.
No matter your industry or your product, evaluators proactively safeguards your AI app in real time. With AI evaluators you gain:
Coralogix’s Evaluators are designed to help you navigate AI risks in product management.
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