The strategy document was primarily crafted for our founding team and investors. The foundational ideas presented in this document played a crucial role in securing up to $X million in funding commitments.

When I came across a company that had an incredible team of amazing generative AI and LLM engineers, I recognized the potential. However, I also realized the pitfalls of a tech first approach – we could easily end up building a solution in search of a problem. Given the vast opportunities within the generative AI space, it was vital to delineate the specific problems we aimed to address. The goal was to articulate a product thesis that solved a significant, tangible problem aligned to the needs of a substantial target market.

This framework ensured problems, solutions, and risks are mutually exclusive and collectively exhaustive (MECE) so that each area can be investigated on its own to ensure rapid diagnosis and root causing of execution problems.

We discovered that many of the challenges we identified weren't confined to just one domain, such as email. We realized that the problems here generalized across any situation where stakeholders’ economic value depends on a high volume of personalized communication IE sales and customer success. This realization allowed us to broaden our aperture and shift from an application-focused product to a more encompassing platform-centric vision.

Leveraging this framework, we could effectively justify our engineering and compute expenses — two areas that can balloon out of control with AI companies. We could also tangibly demonstrate how these investments translate into short and long term business impact.

This strategy led to a faster product development cycle. In just eight weeks, we were able to launch and derive valuable insights to pressure test our product assumptions. We discovered that finetuning LLMs can create magic on a level that we never would have imagined, but this magic wasn’t consistent and was prone to hallucinations (a common problem with generative AI). This knowledge led us to double down on scaling reliable fine-tuning (which ended up becoming a key tech moat), but scope down the launch use case from zero shot email drafting to email templating which significantly reduced hallucination risk.

Aniket Deosthali

I created this product strategy and roadmap to document hypotheses, focus efforts, and structure the chaos of the early days.

When I came across a company that had an incredible team of amazing generative AI and LLM engineers, I recognized the potential

We discovered that many of the challenges we identified weren't confined to just one domain, such as email

Leveraging this framework, we could effectively justify our engineering and compute expenses — two areas that can balloon out of control with AI companies

GenAI Product Strategy and Roadmap at Spiffy.ai

Gen AI Product Strategy Template

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