How Phospho is the solution for teams doing product engineering on top of AI LLMs
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At this point it’s safe to assume that we’re past the point where there is still any debate that gen AI tools - like ChatGPT, Claude and Midjourney - are going to make an impact on the way we work.
The only remaining questions are: “how much?” and “when?”
The world of tech waits for no one and we’ve seen unprecedented rates of adoption and development in the field of AI. According to Statista, the global AI market is projected to reach +$300 billion in 2024.
In the midst of this AI wave, either drown or learn to surf.
It’s understandable then that the surge in AI adoption is only mirrored in the increasing level of AI integrations in applications across industries which presents new opportunities and challenges for product engineers.
With this article we’re aiming to take a deep dive into how product engineering is changing with the rise of AI LLM integration and the role Phospho will play to get the most value from users’ text data - a rich insights source to fuel effective iteration of AI LLMs.
The Evolution of Product Engineering with AI LLMs
Traditionally, product engineering focused on an iterative approach to creating functional, and user friendly products.
Market research, feedback and industry experience would guide product development.
While this is effective and considered best practice, this approach results in longer development cycles, less personalisation, and more manual tasks when compared to development that leverages AI.
Reports suggest 70% of new applications in 2024 integrate AI LLMs. It stands to reason that sooner or later, most products will apply AI in some way, shape, or form and it may very well eventually become user expectation that a product be smart or intelligent.
Apps that do incorporate AI LLMs are understanding users better than by any traditional means which provides a significant competitive advantage in the market.
This has transformed how user interactions are fuelling development of product features and brings more awareness and focus towards how to best understand the data present in your users conversations with the use of text analytics tools like Phospho.
Challenges in Product Engineering with AI LLMs
Product engineers are not only users and beneficiaries of this new tech, they also have the responsibility of figuring out what it means for the products they’re in charge of.
Incorporating AI LLMs into your apps is one thing, but to incorporate them effectively is another…
So how do product engineers get the most out of AI LLM integrations?
Well without the right tools engineering teams might struggle to properly actualise the rich amounts of data readily available to them to optimise performance.
Here are 3 common challenges when product engineering with AI LLMs and how with the right tools they can be leveraged to provide unprecedented amounts of practical insights:
1) Accuracy and relevance
One of the recurring problems when working with LLMs is the ability to produce responses the user wants.
Hallucinations or misleading responses can frustrate users and erode trust. But how can you tell how accurate your AI LLM app’s responses are if you’re not tracking it?
To achieve that, you would need a tool like Phospho that can track users’ responses and extract insights about trends and patterns in their behavior and sentiment as they occur.
This allows for a much faster feedback loop and a means to continuously develop your AI LLM towards providing users with the responses they are looking for.
2) Real time monitoring
Another common source of friction is that engineering teams often lack sufficient insights into how their users are interacting with their LLM app and what they’re trying to achieve.
By using Phospho to extract insights from user conversations, teams can understand usage patterns, contextual factors, and edge cases for better fine tuning of the app to specific user needs.
Real time monitoring in Phospho can also allow for detection of triggers from previous trends that forecast potential issues in advance so teams can address the problem proactively.
3.Model Evaluation
It’s undeniably difficult to accurately evaluate how an AI LLM app’s performance in real world scenarios is directly affecting business objectives without continuous improvement through data driven means.
The result can lead to suboptimal model performance, usage, inefficiency, and difficulty in translating the value of your AI LLM app across to stakeholders.
Tools like Phospho shrewdly allow for the creation of personalized KPIs to automate insights detection relative to specific business needs.
In conjunction with A/B testing and automatic evaluation pipelines for continuous refinement and prompt performance, Phospho uniquely positions itself to empower product engineering teams to monitor, evaluate and improve their AI LLM app in real time.
How Phospho Empowers Product Owners
High performing product engineers require a broad set of skills that go beyond the ability to code - cross functional management, business acumen, UX design principles - so it’s hard to believe that the real value in AI is automating and replacing product engineers altogether.
At Phospho, our goal is to provide engineers and non technical product owners alike the leverage to gain rich insights from their LLM apps.
With this level of accessibility in mind, we built Phospho to be as seamless as possible to add text analytics to your LLM app and get started:
- Create your Phospho account
- Import your data in a project (as easy as Excel or CSV)
- Set up events and get insights on your dashboard
It’s that simple to streamline your insights gathering and iteration cycle.
The Insights-Rich Future of Product Engineering
AI or no AI, the north star for product engineers will remain the same.
So let’s remember that, at the end of the day, it’s not AI that wins, but creating lasting customer value and solving real problems – with AI.
Which is precisely why we built Phospho, to streamline the iteration cycle with data driven insights.
To start leveraging AI for insights into your LLM app users, sign up here to try phospho on your own data!