The Only LLM Evaluation Stack You’ll Need: How We Standardized Testing, Scoring & Tracing at Scale

Why Most LLM Evaluation Workflows Fall Apart at Scale

That’s where we were. We had a list of input questions and golden answers. We wanted to:

What Is LLM Evaluation, Really?

LLM evaluation is the practice of measuring and improving the output quality of a language model. It includes:

Why Evaluation Matters for Fast-Moving Startups

If you’re running a product that uses OpenAI, Anthropic, or any hosted or fine-tuned LLM — you’re probably asking yourself:

A solid evaluation setup gives you:

Our Stack Requirements

We wrote down what we needed before evaluating tools. Our must-haves:

What We Evaluated: Promptfoo, Trulens, and Langfuse

Why We Chose Langfuse for Everything

After testing Promptfoo locally and exploring Trulens for eval logic, we realized something:

Why Langfuse Worked:

Instead of layering multiple libraries and UIs, we unified around Langfuse — one tool for:

How We Use Langfuse Day-to-Day

Here’s how our evaluation workflow looks now:

We do this both:

This lets us detect regressions, spot improvements, and debug specific failure cases quickly.

Mistakes to Avoid with LLM Testing

Tools that support platform engineering include

Conclusion

Practical Benefits We Got

Success Tips from Our Rollout

Final Thoughts: One Stack, Full Loop

The Only LLM Evaluation Stack You’ll Need: How We Standardized Testing, Scoring & Tracing at Scale

That’s where we were. We had a list of input questions and golden answers. We wanted to:

LLM evaluation is the practice of measuring and improving the output quality of a language model. It includes:

If you’re running a product that uses OpenAI, Anthropic, or any hosted or fine-tuned LLM — you’re probably asking yourself:

A solid evaluation setup gives you:

We wrote down what we needed before evaluating tools. Our must-haves:

After testing Promptfoo locally and exploring Trulens for eval logic, we realized something:

Why Langfuse Worked:

Instead of layering multiple libraries and UIs, we unified around Langfuse — one tool for:

Here’s how our evaluation workflow looks now:

We do this both:

This lets us detect regressions, spot improvements, and debug specific failure cases quickly.

Tools that support platform engineering include

Related Blogs

The Only LLM Evaluation Stack You’ll Need: How We Standardized Testing, Scoring & Tracing at Scale

That’s where we were. We had a list of input questions and golden answers. We wanted to:

LLM evaluation is the practice of measuring and improving the output quality of a language model. It includes:

If you’re running a product that uses OpenAI, Anthropic, or any hosted or fine-tuned LLM — you’re probably asking yourself:

A solid evaluation setup gives you:

We wrote down what we needed before evaluating tools. Our must-haves:

After testing Promptfoo locally and exploring Trulens for eval logic, we realized something:

Why Langfuse Worked:

Instead of layering multiple libraries and UIs, we unified around Langfuse — one tool for:

Here’s how our evaluation workflow looks now:

We do this both:

This lets us detect regressions, spot improvements, and debug specific failure cases quickly.

Tools that support platform engineering include

Frequently Asked Questions

From MVP to Production: Infrastructure Checklist for Seed Startups

What Every Seed to Series A CTO Should Prioritize in Cloud Infrastructure

Choosing AWS Compute: EC2, Lambda, Fargate, or EKS

Related Blogs

Frequently Asked Questions

From MVP to Production: Infrastructure Checklist for Seed Startups

What Every Seed to Series A CTO Should Prioritize in Cloud Infrastructure

Choosing AWS Compute: EC2, Lambda, Fargate, or EKS