3 steps to do AI product optimization with Phospho

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You’ve launched your product and acquired some customers. You’re heading in the right direction, but you’ve only scratched the surface. Now, the real work begins.

Here’s the thing: Your product will always be evolving through iteration.

But its performance and success will heavily rely on keeping what users already love about the product and continuously applying small changes through data-led iterations.

Incrementally building a product that addresses pains and bottlenecks in this way is product optimization.

What is product optimization?

As stated above, product optimization is simply the process of improving a product, in this case, LLM apps, with small incremental steps. Thus, it is all those smart changes, driven by data, that actualise large improvements over time.

Why is product optimization important?

You will obviously need to keep abreast of a fast-changing environment and the market, in the AI and LLM app world. So-called minor changes to products make a huge difference in:

• Improved customer satisfaction: The more we know about our users, the more aligned iterations will be with what they really need. For instance, in 2010, Airbnb did some research and realised users were extremely frustrated by waiting for hosts to approve booking requests. Their reaction was the launch of a new feature called "Instant Book", which incredibly resulted in an instant increase in bookings and revenues.
• Boosted conversions and revenue: Effective optimization, catering to the real needs of users, keeps users engaged and coming back to your app. This obviously leads to higher retention rates and more opportunities to turn all these people into loyal customers who, in turn, recommend other users.
• High Performance: AI analytics tools clearly define and pinpoint performance bottlenecks so teams can work on the solution. In this way, we all will have the assurance of a more stable and responsive app, which minimizes the risk of any user frustration or bad reviews that can be bad for our brand image.

How do we actualize product optimization? (Tips and Guidelines)

It is a fact that product optimization deploys qualitative data to understand the nature of your user's pain points. However, when it comes to the evaluation of the changes, it deploys quantitative insight from experimentation.

So how will we set up experiments for product optimization? Let us walk through some of the main tips and guidelines:

1. Set explicit goals, including User behaviour and any breakpoints in their journey. Be very clear about what you are trying to achieve through product optimization. This might include increasing user engagement, raising conversion rates, enhancing user experience, etc.
2. Success Metrics and Timeframe: You will need metrics to measure whether the team's work has actually improved your product. In most cases, this would be something like whether users are capable of performing a certain task on your product with less time needed.
3. The right tech stack: Optimisation is data-driven. Getting that data will require tools. Analytics platforms like Phosphor are designed to track insights, especially for LLM applications. That data will let you find out what is broken or where improvement lies and understand user needs and preferences much more lucidly.
4. Test one thing at a time: What works best against your success criteria?.
5. Test with the right users. You'll also want to share this with your user base, that your team's constantly looking for ways to make your product more valuable to them. Messages to end-users while you're optimising product are very instrumental in sending an important signal: you care about them and strive to make your product as beneficial to them as possible.
6. Analyse and decide Pour over the collected data to identify patterns and trends, or areas of improvement. Identify common pain points, user behaviour, zeroing in on areas where improvement is wanted.

Based on that analysis, prioritise the enhancements that would have the greatest impact on attaining your goals, guided by factors such as user needs, business objectives, and technical feasibility.

Essentially, it's about regularly gathering user feedback and conducting A/B tests to know what's working and what's not.

The Need for a New Approach in AI Startups

There are big challenges in current practice to actualize product optimization for today's startup landscape. The AI industry has increased by 270% in the last four years alone, of which LLMs were a vital contribution.

AI startups are disrupting industries by making smart algorithms go through heaps of data within seconds. This gain in velocity helps them make quick decisions about market trends and customer choices. More specifically, LLM apps are really pushing the boundaries of how much any one of us can do with language while at the same time opening up new possibilities.

Limitations of Traditional Optimisation Tools for LLMs

Data analysis is instrumental in product optimization, as it provides a business or developer with objective, quantifiable information on the product and the market. Thus, it may help your team realize areas where the product is working well and others where improvement is needed.

However, old conventional tools weren't designed to acquire this data and the associated insight from modern AI LLM apps. There’s a great deal of difficulty in obtaining real-time user data, and it results in slow iteration cycles, which can be costly.

To beat these challenges, we’ve built Phospho, to allow AI startups to easily monitor and evaluate their LLM apps in real-time to fuel fast and effective iteration.

The world of making products is changing fast with AI startups and LLM apps leading the way. This change means we need special tools to handle the unique problems these new technologies bring.

How Phospho can help run continuous product optimizations

Phospho is an open-source text analytics platform designed to help AI startups optimize their LLM apps by providing real-time insights and continuous evaluation.

Our platform focuses on quick testing, trying new things, and designing with users in mind to help build products that meet what your users really need.

To do this, we offer features that let you watch how users engage in real time and evaluate ways to optimise and fine-tune performance, helping with ongoing updates. This is key for LLM apps that need to keep up with changing language and what users want.

Phospho fills in the gaps left by traditional tools like Mixpanel and Google Analytics to help you improve aspects of your products that integrate LLMs. For example:

1. Automatically detect and set up custom events that trigger webhooks when detected. For example, receiving a slack message when a user discusses a topic.
2. Phospho automatically labels a task as a success or failure based on previously set criteria. Feedback from you or your users can improve the task. Therefore, when a new task is logged to phospho, it can be classified as a successful or unsuccessful interaction based on your users’ (or your team’s) custom criteria.
3. Release several new versions and use Phospho’s A/B testing to determine which ones perform better.

It keeps product teams connected to the user experience and helps them understand what needs to be optimised or fixed to better solve customer problems, increase AI product performance, and remain competitive in the fast-changing landscape of the LLM app market.

If you’re creating an LLM app and want to gain untapped insights from your text data, sign up here!

3 Steps to Optimise Your LLM App with Phospho

Optimizing an LLM app needs a plan. We've created a three-step method (while using our text analytics platform) to optimize your LLM app’s performance:

Step 1: Monitor User Interactions in Real Time

We start by watching how users use your LLM ****app. Phospho’s logging feature lets you track and log (any and every) user input to identify issues or trends as well as continuously fine-tune the performance of your LLM app.

We also facilitate annotations on the fly (in the app), meaning you can ‘flag’ certain logs for other team members to view and collaborate on without the need for any coding knowledge.

Step 2: Detect the Signal in the Noise

We then analyze the data to uncover key insights. Phospho can automate insights detection from custom KPIs to identify anomalies, trends, and patterns in user interactions.

This helps teams see how users move through the app, understand their paths, and find areas to optimize.

Phospho can also allow for the detection of triggers from previous trends that forecast potential issues in advance so teams can address problems proactively.

Step 3: Evaluate, Test, and Iterate

We then test our ideas and analyze the results. The cycle of testing and improving lets us continuously optimize our ****products and AI model performance.

You will need to choose the right metrics to help evaluate the effectiveness of tests to optimize your LLM app. Phospho lets you set tailored metrics and events aligned with your app’s unique goals so you can more precisely monitor LLM performance aligned with those requirements.

With our A/B testing functionality, you can quickly test how effectively different versions of your AI model perform and iterate based on the results.

By following these steps, we ensure that your LLM app meets user needs by combining data-driven decisions with ongoing improvement.

We intentionally built Phospho to be as seamless as possible to add text analytics to your LLM app and get started quickly:

1. Create your Phospho account
2. Import your data in a project (as easy as Excel or CSV)
3. Set up events and get insights on your dashboard

It’s that simple to streamline your insights gathering and iteration cycle.

To leverage AI for insights into your LLM app users, sign up here to try phospho on your own data!

Conclusion

It may be small changes, but that is what separates a good product from an amazing one.

Companies in tune with their customers show an elevated shift in sales by up to 85% compared to their peers. Those who do 6x better each have a clear, well-defined ethos or purpose. With tools like Phospho, AI startups can streamline iteration cycles through data-driven decisions that were otherwise hard to come by.

In the future, the capability to optimize AI products in this way will set companies apart. It’s how AI startups can optimize products better, engage users more, and capture a bigger market share.

For those building an application based on an LLM and looking to gain untapped insights from their textual data, sign up here!