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Choosing Your LLM: A Product Thinking Approach

Original Thesis: Evaluating Open-Source vs. Closed-Source LLMs Through the Lens of Product Thinking

 

Intro:

Our technology advisor, Amit Ghadge covered LLM selection brilliantly in his "Adaptable Generative AI Applications" blog. If you haven't read it yet, do – it's invaluable for navigating the open-source vs. closed-source debate.

 

This post digs deeper, examining LLM selection through a product thinking lens. We'll explore how user-centricity, business value, and an iterative approach influence your decision. We'll also discuss why LLM-agnostic applications are crucial in this evolving landscape.

 

User-Centricity:

Closed-source LLMs seem simpler, winning out on "ease of use." But user-centricity extends beyond LLM convenience. Consider your application's end user. Open-source LLMs offer more customization, potentially better serving their needs.

 

Business Value:

Amit's blog features a great graph summarizing the pros and cons of each, offering valuable insights. We will just copy that here again as that speaks to the business value and the decision factors by itself.


Iterative Approach:

Product thinking emphasizes focus and iteration. There's no one-size-fits-all answer here. Consider your application's goals, scale, and flexibility needs. Our advice:

 

  1. Jump in! Build a Minimum Viable Product (MVP) with whichever LLM feels right.

  2. Stay focused on your overarching goal.

  3. Learn from each iteration. Build LLM-agnostic applications so learnings can be applied even if you switch LLMs later.

 

Join the Conversation:

Share your thoughts and experiences! If you'd like to delve deeper on this platform, reach out – we're happy to host your content.

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Amit's original blog post is the first post on this platform to have crossed 400 views!!

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