Productside Stories

Navigating AI Product Management with Marc Klingen: Key Insights from Langfuse

Featured Guest:

Marc Klingen | Co-founder at LangFuse
05/11/2024

Summary

In this episode, Rina Alexin sits down with Marc Klingen, co-founder of Langfuse and Y Combinator alumnus, to explore the evolving world of AI product management. Marc shares insights on building reliable AI applications, selecting high-impact use cases, and tracking quality, cost, and latency to drive measurable outcomes.

He highlights the importance of understanding the real cost of AI implementations and how teams can balance innovation with business value. Marc also discusses common missteps in AI projects—from focusing on the wrong metrics to underestimating prompt engineering—and how to avoid them by designing effective feedback loops and evaluation systems.

The conversation offers a rare inside look at how Langfuse helps engineering and product teams scale safely, improve visibility, and move faster in a rapidly changing LLM ecosystem.

Takeaways

  • Prioritize use cases that drive measurable business or user value.

  • Product managers must understand trade-offs between cost, quality, and latency.

  • AI product success depends on balancing risk, brand safety, and user trust.

  • Use implicit and explicit feedback loops to measure quality and user satisfaction.

  • Don’t overengineer for generic problems—focus on niche, high-value use cases.

  • Abstract your LLM integrations early to stay agile as technology evolves.

  • Product managers benefit from learning prompt engineering fundamentals.

  • Optimize user experience during latency—turn waiting time into engagement.

  • Embrace experimentation; learn fast and iterate on real data.

  • The best way to learn AI product management? Experiment, iterate, and build

Chapters

00:00 Introduction and What is Langfuse
02:04 Langfuse’s Customer Base and Users
02:44 Understanding and Tracking AI Costs
04:13 Selecting the Right AI Use Cases
06:43 Managing Risk and Brand Safety in AI
07:50 Measuring Quality, Cost, and Latency
10:15 The Role of “LLMs as Judges” in Evaluation
11:48 Specialized Models and Niche AI Applications
12:45 Adapting to Rapid AI Evolution
16:24 Common Missteps in AI Implementation
18:33 The Value of Prompt Engineering for Product Managers
19:22 Latency, Quality, and the User Experience
20:55 Getting Started in AI Product Management
23:32 Learning Resources, Tools, and How to Connect with Marc

Keywords

AI product management, large language models, Langfuse, product innovation, Rina Alexin, Marc Klingen, Y Combinator, cost optimization, prompt engineering, open-source AI tools, latency optimization, feedback loops, AI evaluation metrics, user experience design, LLM agents, data-driven decision making, AI adoption strategy, generative AI

Introduction and What is Langfuse

Rina Alexin | 00:00–01:06

Hi everyone, and welcome to *Productside Stories*, the podcast where we reveal the lessons learned from product leaders and thinkers all over the world. I’m your host, **Rina Alexin**, CEO of Productside. Today I’m joined by **Marc Klingen**, Co-founder of **Langfuse**, a Y Combinator-backed open-source platform for building reliable LLM applications. Marc, welcome!

Marc Klingen | 01:06–02:04

Thank you, Rina! Together with my co-founders, we started Langfuse after realizing how hard it was to understand, measure, and improve AI-driven applications — especially beyond simple chatbots. We needed a way to monitor, evaluate, and iterate on complex LLM systems. That’s what became Langfuse: a suite of developer tools that helps thousands of teams, from startups to enterprises, ship working LLM products faster.

Langfuse’s Customer Base and Users

Marc Klingen | 02:04–02:44

We’re typically adopted first by engineering teams, since they’re closest to the challenges of monitoring cost and understanding system behavior. But over time, more product managers and leaders join — they care deeply about metrics, user feedback, and ROI. Langfuse provides visibility across all those dimensions.

Understanding and Tracking AI Costs

Marc Klingen | 02:44–04:13

Predicting AI costs is tricky. It depends on use case, context, and scale. However, for many B2B use cases, cost per use isn’t the biggest issue — if the value is high, the economics work. The challenge is **tracking costs over time**, understanding how new model generations affect efficiency, and using techniques like **prompt optimization** or **model distillation** to keep costs under control as usage grows.

Selecting the Right AI Use Cases

Marc Klingen | 04:13–06:43

Use case selection is critical. PMs and leaders must weigh **risk versus return** — both to users and the business. Some AI applications bring brand risk, especially if they hallucinate or generate unverified responses. That’s why setting clear **acceptance criteria** and **boundaries** for open-ended systems is vital. Guide users to the right interactions and align expectations.

Managing Risk and Brand Safety in AI

Marc Klingen | 06:43–07:50

Hallucinations are a major brand risk, especially in regulated industries. For example, legal or healthcare applications must be grounded in verified sources. Langfuse helps teams evaluate and monitor these risks through structured evaluation pipelines.

Measuring Quality, Cost, and Latency

Marc Klingen | 07:50–10:15

Langfuse tracks three key categories: **quality, cost, and latency**. – *Quality* can be measured through **implicit feedback** (like how users interact) and **explicit feedback** (like manual labeling). – *Cost* ensures efficiency. – *Latency* affects user experience. Implicit feedback loops — like whether a support agent edits an AI-suggested reply — can reveal how well a system performs in production.

The Role of “LLMs as Judges” in Evaluation

Marc Klingen | 10:15–11:48

A growing trend is using “LLMs as judges.” Teams can prompt a separate model to evaluate whether a response is grounded in factual data or hallucinates. While this approach is promising, you still need **manual baselines** to trust results. Automated evaluation without benchmarks is risky.

Specialized Models and Niche AI Applications

Marc Klingen | 11:48–12:45

We’re seeing a shift toward **specialized, fine-tuned models**. Large general-purpose models are great, but niche models — like those for biology or law — deliver better results for focused domains. Expect to see more purpose-built LLMs trained for specific sectors.

Adapting to Rapid AI Evolution

Marc Klingen | 12:45–16:24

The AI field evolves fast — and that’s an opportunity, not a threat. With proper **abstraction layers** and **monitoring**, teams can upgrade to newer models quickly and safely. The key is to architect systems for flexibility and invest in evaluation pipelines so you can measure the impact of every model change.

Common Missteps in AI Implementation

Marc Klingen | 16:24–18:33

A common mistake is over-optimizing one approach without exploring alternatives. Teams should **experiment broadly** before committing. Another misstep is coupling software engineering and prompt engineering too tightly. Decoupling these workflows — using prompt management tools — allows faster iteration and collaboration between PMs, engineers, and domain experts.

The Value of Prompt Engineering for Product Managers

Marc Klingen | 18:33–19:22

Prompt engineering is a superpower for PMs. It lets them shape behavior without deep coding. Langfuse and similar tools help manage prompts efficiently, balancing performance with speed.

Latency, Quality, and the User Experience

Marc Klingen | 19:22–20:55

As AI products scale, **latency becomes critical**. A few seconds of delay can frustrate users. Optimize first-token speed and create thoughtful **loading experiences** to maintain engagement — even when responses take longer.

Getting Started in AI Product Management

Marc Klingen | 20:55–23:32

For PMs getting started, I’d suggest exploring **Langfuse’s Library** — curated readings and frameworks for building LLM-based products. Combine that with practical experimentation: try prompt engineering, explore open-source models, and study real-world AI UX design.

Learning Resources, Tools, and How to Connect with Marc

Rina Alexin & Marc Klingen | 23:32–End

**Rina:** Marc, this was an amazing discussion. Thank you for sharing your wisdom with our listeners. **Marc:** Thank you, Rina! You can find me on **LinkedIn** or **Twitter**, or visit **Langfuse.com** — we share lots of open resources for AI product teams. **Rina:** Perfect. And for more tools and courses like our *AI Product Management Program*, visit **Productside.com**. Until next time, keep leading and keep learning.