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Why Product Managers Need to Dive Deep into the World of LLMs

In today’s rapidly evolving tech landscape, staying ahead of the curve is an indispensable requirement for product managers. One technological advancement that has been making waves is Large Language Models (LLMs). As a product manager with expertise in creating products that utilize generative AI, I cannot stress enough the importance of understanding the nuts and bolts of LLMs. This understanding can significantly impact product use cases, AI safety, resource needs, time-to-market, maintenance needs, and costs. In this blog post, I will shed light on why product managers need to acquire a deep understanding of LLMs and the possible solution space.

Unraveling the Potential of LLMs

As of June 2023, creating text-based generative AI solutions has become more accessible than ever. With techniques such as prompt engineering, plugins, vector indexing, model fine-tuning, and training from scratch, there are endless possibilities.

Use Cases and AI Safety

Understanding the algorithmic intricacies of LLMs can help you identify suitable use cases. For instance, using LLMs for content generation may require different configurations compared to using them for customer support chatbots. Additionally, knowledge of how these models work will enable you to anticipate and mitigate the risks associated with AI systems, ensuring that they align with ethical standards and do not inadvertently cause harm or perpetuate biases.

Resource Needs

Deploying LLMs can be resource-intensive. Knowing the computational demands of different models will allow you to make informed decisions regarding the trade-offs between model performance and resource constraints. Additionally, it will enable you to choose the optimal deployment environment, whether it be cloud SaaS, hosted, on-premises, or on the edge.


A firm grasp of the underlying technology will allow you to accurately estimate the time it will take to train or fine-tune the models, thus ensuring a more realistic product roadmap. By knowing what’s feasible and what isn’t, you can avoid unnecessary delays and deliver your product to market in a timely manner.

Maintenance Needs and Cost

Understanding how LLMs are trained and the data they require can give you insights into the maintenance they might need. Continuous training might be necessary to keep the model up-to-date. This, in turn, can impact the cost structure of your product. Being able to anticipate these needs will allow you to plan for them effectively.

Diverse Solution Space

Given the plethora of tools and techniques available, as product managers, we must be able to navigate this diverse solution space efficiently. The models can be proprietary, open-source, or custom. The datasets can be proprietary, open-source, custom prepared demonstration data, custom prepared comparison data, or custom prepared custom RLHF prompt data.

Eliminating the Infeasible

Not every combination of these options is possible or practical. Knowledge of LLMs will empower you to eliminate a large part of the solution space that is either infeasible or not cost-effective. For instance, training a large model from scratch might not be necessary if fine-tuning an open-source model can fulfill the same purpose at a fraction of the cost.

The Power of Early Decision Making

By understanding the range of options and their implications, product managers can make critical decisions early in the product development process. This could potentially save millions of dollars in technology investment. Imagine embarking on a path to train a model from scratch, only to find out later that an equally efficient solution could have been achieved through fine-tuning. The time and resources wasted in such a scenario are staggering.

In Closing

The era of LLMs is upon us, and as product managers, we need to adapt to this new reality. By diving into the details of LLM algorithms, deployment options, and datasets, we can unleash the full potential of these powerful tools. This knowledge not only enables us to create innovative products but also ensures that we do so in a way that is efficient, safe, and cost-effective. The return on investment for taking the time to understand LLMs is enormous. It is not just a nice-to-have skill; it is a must-have in the toolbox of every forward-thinking product manager.

This is where the Generative AI for Product and Business Innovation LIVE program comes to your help. In this program, you will learn about Generative AI lifecycle, use cases, and limitations, enabling participants to identify and solve business problems with Generative AI. Furthermore, you will learn about the AI algorithms, MLOps lifecycle, including the deployment aspects. Join now to become a business professional with Generative AI expertise and harness its potential for your business. Watch the students testimonial and sign up for the next cohort now at

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