Product Lifecycle Management for Software Development for LLM-Based Products

Gartner predicts that 30% of Generative AI projects will be abandoned after Proof of Concept by the end of 2025!

Why is that so?

Because while a PoC can demonstrate the viability of an idea, it’s the Product Lifecycle Management (PLM) that transforms that idea into a sustainable, scalable product.

Without PLM, that PoC often gets stuck in what we call “AI purgatory”— exciting on paper but not equipped for real-world deployment.

PLM is the bridge that turns a PoC into a fully-fledged product.

And for those not leveraging it properly? They’re playing with fire in an era where speed, quality, and agility mean everything.

So, the question isn’t if you need PLM for your LLM-based products, but rather how well you’re leveraging it to maintain your competitive edge.

The Unique Challenges of LLM-Based Products

Building an LLM-based product comes with its own set of complexities, most of which don’t exist in traditional software development.

Rapid AI Evolution 

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AI models evolve fast. Today’s state-of-the-art LLM could be obsolete in six months.

For example, every new release of a language model like GPT significantly improves on the last — requiring constant upgrades, re-training, and deployments.

Hence, you must ensure that your AI-driven products can evolve without massive disruptions.

The question isn’t whether you need to update your LLM-based products, but how frequently and efficiently you can do so.

Scalability Concerns

Scaling software is already tough. Scaling AI-driven systems? It’s another beast entirely.

Training an LLM requires not just large datasets but also substantial compute power. The operational cost alone for training and inference can be staggering.

Add in the need to handle user spikes, real-time model updates, and data privacy concerns, and you’ve got a perfect storm.

Data Management and Ethics

Unlike traditional software, where data might be static or limited to usage logs, LLM-based products live and die by the data they consume.

Worse, the quality of these models hinges on constant data curation, cleaning, and labeling to stay relevant.

As new trends, languages, or regulations emerge, your product must incorporate them immediately — or risk delivering outdated or biased outputs.

Balancing Model Performance with User Needs

Developing an LLM-based product often means walking a fine line between technical innovation and practical utility.

CEOs and CTOs are constantly faced with the challenge: how do you ensure your product leverages the most advanced AI while still delivering tangible, user-driven results?

Too much emphasis on the latest AI capabilities without considering user experience can lead to a product that impresses engineers but confuses customers.

How PLM Frameworks Adapt to LLM-Based Software Development

Traditional PLM frameworks weren’t designed with AI in mind, but they can be adapted to handle the nuances of LLM-based products.

Below are the key stages, each with a focus on technical considerations and operational complexities:

AI-Specific Lifecycle Stages

For AI products, particularly those built on LLMs, the product lifecycle stages require a shift in focus:

Collaboration Between Data Science, DevOps, and Business Teams

One of the unique aspects of managing an AI product lifecycle is the intense collaboration required across various teams.

Product managers, AI engineers, data scientists, and even legal teams must align on every decision, from model development to user deployment.

This cross-functional effort is often the difference between a scalable, ethical product and one that fails to meet expectations.

Given the complexities, some best practices can ensure smoother management of LLM-based software.

Key Metrics and KPIs for LLM Product Lifecycle Management

Monitoring the right metrics is essential to keep your LLM-based product on track.

Accuracy, precision, and recall aren’t enough.

You need deeper insights into how your models perform across different user demographics, geographies, and contexts.

Track how users interact with your AI features. Are they finding value, or are there friction points that reduce adoption?

Engagement metrics can inform not just feature development but also model refinement.

AI updates need to be faster and more efficient.

Optimizing the handoff between data science, DevOps, and product teams is critical for minimizing delays.

Training and deploying LLMs are resource-intensive. Monitoring the balance between computational costs and business ROI is essential.

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You should also read ➡️ What is the Cost of Training LLM Models? ↗️

Ensuring that collaboration across teams and the model lifecycle runs smoothly can be the key difference between success and failure.

Strategic Considerations for the Future of LLM-Based Products

LLMs are driving hyper-personalization like never before.

From chatbots offering tailored recommendations to analytics tools that adapt based on user input, LLM-based products will only become more personalized.

Your PLM strategy needs to accommodate the shift towards AI that understands and adapts to individual users.

The rise of low-code and no-code AI platforms will redefine how companies build LLM-based products.

As these platforms make AI more accessible to non-technical teams, product lifecycles will shorten, and the iteration process will become more democratized.

There’s growing concern about the energy costs of training large LLMs.

Incorporating sustainability into your PLM process isn’t just a PR move — it’s an operational imperative.

Models should be built, trained, and deployed in a way that minimizes carbon footprints.