Building the AI Stack: Insights from Particle’s Sara Beykpour Shared by April Underwood

In a recent LinkedIn post, April Underwood highlights key insights shared by Sara Beykpour, co-founder of Particle, regarding the rapid evolution and practical construction of AI stacks. Underwood frames the discussion around a new piece from Adverb Ventures’ “Field Notes” series, which features Beykpour and Jennifer Jang detailing their experiences building Particle’s Large Language Model (LLM) stack. The post emphasizes the lack of a pre-existing playbook when Particle began its journey in early 2023, contrasting it with the significantly changed landscape two years later.

Underwood shares that the “Field Notes” piece offers a candid look at the decisions founders face when scaling AI products in real-time. A central theme discussed by Beykpour and Jang, as presented by Underwood, revolves around the critical build versus buy decisions for AI infrastructure.

“When Sara Beykpour started building Particle in early 2023, there was no playbook — no dashboards, no eval tools, no prompt libraries. Two years later, the AI stack looks entirely different.”

Navigating the Build vs. Buy Dilemma in AI Stacks

April Underwood points to the core tension between control and speed that founders grapple with when deciding whether to build custom AI components or leverage existing solutions. As Underwood relays from the Particle founders’ experience, this decision is fundamental to the pace of innovation and the ability to maintain a competitive edge.

The discussion, as highlighted by Underwood, delves into specific areas of the AI stack:

• Build vs. Buy: Exploring the trade-offs between gaining control through custom development and achieving speed with off-the-shelf tools.
• Prompt and Evaluation Design: Detailing strategies for engineering AI reasoning capabilities effectively.
• Evals vs. A/B Tests: Examining methods for maintaining a solid baseline performance while iterating and improving AI models.

Underwood emphasizes that Beykpour and Jang provide a practical, founder-focused perspective on these complex choices.

The Importance of Prompt Engineering and Evaluation

Further elaborating on the insights shared by Underwood, the “Field Notes” piece underscores the significance of prompt design and evaluation methodologies. Engineering for reasoning is presented as a key differentiator for AI products.

“They get into: 🧰 Build vs. buy → control vs. speed ⚙️ Prompt & eval design → how to engineer for reasoning 🧩 Evals vs. A/B tests → keeping your baseline solid while you climb higher”

As April Underwood conveys, the founders’ candid approach in the article aims to equip other entrepreneurs with actionable knowledge. The emphasis on both prompt design and robust evaluation methods suggests a sophisticated understanding of how to refine AI performance beyond initial deployment.

Real-time Scaling and Founder Trade-offs

Ultimately, Underwood frames the Adverb Ventures piece as a valuable resource for understanding the realities of building AI companies today. The insights from Particle’s journey, as shared on LinkedIn by Underwood, reveal the constant negotiation between different strategic priorities.

According to Underwood, the article serves as:

“A candid look at the trade-offs founders are making as they scale AI products in real time.”

This perspective is crucial for anyone looking to build or invest in the rapidly advancing field of artificial intelligence, offering a glimpse into the practical challenges and innovative solutions emerging from the front lines of AI development.