Offload Observability of your AI Applications

Building AI-powered systems is fundamentally different from creating traditional software because AI models are probabilistic and can behave unpredictably. For example, a customer support chatbot might answer most questions correctly but give completely irrelevant responses when faced with a slightly unusual query. In production, these failures can lead to poor user experiences and lost revenue. Ensuring reliability requires more than just initial testing. It demands continuous monitoring, thorough debugging, structured testing, and real-time observation in production.

In this blog, we will explore the unique challenges of building AI applications, including the lack of transparency in model decisions, the difficulty of pinpointing the source of failures, and the risk of performance degrading over time. We will also look at best practices for improving visibility into AI workflows, managing evolving prompts, and tracking performance metrics so you can move from experimental prototypes to dependable and production-ready AI systems.

Even with a strong model and a well-designed application, AI systems can fail in ways that are difficult to predict or diagnose. Unlike traditional software, issues often arise from the interplay between the model, the data, and the surrounding infrastructure after the testing is done because of the non-deterministic nature of AI. Below are some of the most common challenges teams face when working with AI in real-world environments:

1. Observability: Once deployed, AI applications often operate as black boxes. Teams lack visibility into how users interact with the system, what types of inputs are being processed, and where the model fails. This makes it difficult to identify recurring issues or opportunities for improvement.
2. Testing Approach: Traditional unit tests are ineffective for AI because outputs can vary based on slight changes in input or context. Manual testing is time-consuming and unreliable. Moreover, AI systems can degrade due to changes in data distribution, evolving user behavior, or updates to underlying models. Continuous and automated evaluations that can assess model performance across real-world scenarios are necessary for AI applications.
3. Latency & UX Monitoring: AI systems are inherently slower than traditional applications due to multiple processing steps such as context retrieval, model inference, and post-processing. Each of these steps can introduce delays. Without detailed timing breakdowns, it is difficult to identify which part of the flow is responsible for latency. Teams need to monitor each section of the pipeline separately to pinpoint bottlenecks and improve the overall user experience.
4. User Feedback: Traditional applications typically produce outputs that are either clearly correct or clearly incorrect. AI applications can generate responses that look acceptable on the surface but do not actually match what the user intended. These subtle errors are difficult to identify. To address this, users need a way to provide detailed and structured feedback that captures more than just a simple yes or no. This allows teams to better understand where the model is missing the mark and make meaningful improvements.
5. Prompt Management: In AI applications, small changes in prompts can lead to significant improvements in output quality, tone, or task completion. However, in many systems, prompts are hard coded, making it slow and risky to experiment with variations. To iterate quickly and respond to user feedback or changing requirements, teams need the ability to edit, version, and deploy prompt changes directly from a central interface without modifying code or redeploying the application.

When we started building AI-powered applications, we quickly realized that visibility into what the model was doing in production was absolutely essential. It wasn’t just about catching failures. We needed to understand how users were interacting with the system, how prompts were behaving, where latency was coming from, and whether the model was actually helping people.

Instead of building custom infrastructure ourselves, we looked for tools that could help us move fast without sacrificing insight. That’s when we found Langfuse, and we’ve been using it ever since to monitor, debug, and improve our AI workflows.

Here’s what that looks like in real scenarios:

1. Observability & Investigation

We are notified every time our chatbot is unable to answer a question so we can investigate the issue. For example, recently a user asked the following question:

As you can see in our Langfuse log, the output of the model was “Sorry, I don’t know how to help with that”. When we click into the “vector-store” tab:

It is clear that no relevant document for “Real Madrid” exists in our database. If we want the bot to answer questions like this, all we need to do is add the right data. This detailed observability provided by Langfuse helps us quickly narrow down the issue and apply the fix in the right place without any guesswork.

2. Accuracy & Quality

To ensure high-quality responses, we’ve integrated Langflow to create a robust test suite that every model update must pass before deployment. These tests use an LLM-based judge to automatically evaluate model outputs for critical attributes such as helpfulness, accuracy, relevance, and absence of PII. As a sanity check, we also ensure that our answers contain all the important and expected keywords.

This testing doesn’t stop at deployment. Even in production, every AI response is continuously evaluated by an LLM judge to monitor for issues in real time. If a response fails to meet the expected standards, for example, if it’s inaccurate, irrelevant, or unhelpful, the developer receives a notification. This enables us to catch edge cases early, identify blind spots in our system, and continuously improve our application based on actual usage.

3. Latency & User Experience

AI applications often involve multiple components like retrieval, model inference, post-processing, and API calls. With tools and SDKs constantly evolving, it becomes difficult to know where performance issues are coming from without detailed tracking.

We monitor latency at a granular level for each component in the pipeline. We measure P99, P95, P50, and P1 latency for steps like embedding generation, vector search, model response time, and custom logic. This gives us a clear view of where slowdowns occur and how performance changes over time.

In addition to aggregate metrics, we capture latency data for each individual call. This lets us quickly investigate outliers and debug user-specific issues without relying on guesswork. 

With these details we can decide where caching can help and which part of our code we need to focus on first to get the most improvements. This level of insight has helped us remove unnecessary delays, catch regressions early, and ensure a smoother user experience across the board.

4. Nuanced User Feedback

Every AI response includes an option for users to leave feedback. This goes beyond a simple thumbs up or down. Users can also add comments to explain why they were satisfied or not. When feedback is submitted, developers receive all the relevant details together: the user’s input, the AI’s output, evaluation results, and the user’s written feedback. This gives a complete view of the issue and helps the team quickly understand and improve the system. This kind of detailed feedback is essential for catching subtle issues and making meaningful improvements based on real user experience.

5. Separated Prompt

Instead of hard coding prompts, we store them in Langfuse and fetch them dynamically at runtime. This setup allows us to update prompts instantly without touching the code base or redeploying the app. Whether it’s improving clarity, fixing issues, or running experiments, changes can be made safely and quickly. This flexibility has been key to iterating fast and responding to feedback in production.

Langfuse is a powerful tool for improving AI observability, managing prompts, and streamlining debugging and testing workflows. It helps teams ensure their AI systems remain reliable and efficient.

However, observability is only one part of building successful AI applications. Deploying AI at scale also requires expertise in security, infrastructure, scalability, and compliance. That is where we can help. We specialize in designing, deploying, and maintaining AI solutions that deliver measurable results. Reach out to us at [email protected] to start a conversation about how we can bring AI into your infrastructure.