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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

Most fast-growing teams building on LLMs hit the same wall: you can prompt, tweak, and test manually for a while — until it’s impossible to tell what’s actually improving performance.

That’s where we were. We had a list of input questions and golden answers. We wanted to:
  • Tune prompts and track improvements over time

  • Evaluate changes across versions and conditions

  • Score LLM answers reliably (not just by feel)

  • Avoid manually testing every time we deployed

We’d already integrated Langfuse for logging and tracing. But we weren’t sure if we needed a separate tool (Promptfoo? Trulens?) to handle structured evaluation and prompt testing.

After exploring options, we ended up doing it all — prompt testing, LLM-as-a-judge scoring, chain tracing, and historical comparisons — in Langfuse alone.

This post explains why, how, and what you can borrow from our decision.

What Is LLM Evaluation, Really?
LLM evaluation is the practice of measuring and improving the output quality of a language model. It includes:
  • Defining expected outputs ("golden answers")

  • Comparing responses from different prompt versions

  • Scoring results using human feedback or automated evaluators (like another LLM)

  • Tracking changes over time

  • Observing model behavior in live systems

LLM evaluation is not just about testing — it's about making prompt design measurable and repeatable, especially when the model is part of a production system.

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:
  • Are our new prompt changes actually better?

  • Why did the output quality degrade last week?

  • What caused the hallucination in that support answer?

  • Can we roll back a bad prompt like we roll back code?

When you’re scaling fast, guessing is too risky.

A solid evaluation setup gives you:
  • Confidence in releases — you can test prompts before shipping

  • Visibility into production — know what prompts, models, and chains are doing in the wild

  • A feedback loop — so you get better with every iteration

"LLMs are not black boxes — unless you treat them like one."

Our Stack Requirements
We wrote down what we needed before evaluating tools. Our must-haves:
  • Regression testing with golden questions and expected answers

  • Support for prompt version comparison

  • Both manual and LLM-as-a-judge evaluations

  • Integration with our Node.js and LangChain.js apps

  • Full chain and tool trace visibility

  • CI/CD compatibility (for catching regressions)

We were also clear on what we didn’t want: fragmented tooling, Python-only libraries (we’re on Node.js), or massive overhead.

What We Evaluated: Promptfoo, Trulens, and Langfuse

Here’s how the three major options stacked up:

Feature

Langfuse

Promptfoo

Trulens

Prompt version comparison

Yes

Yes

Workaround

Golden dataset eval

Yes

Yes

Limited

LLM-as-a-judge support

Yes

Yes

Yes

Manual scoring

Yes

CLI only

Code-level

Full LLM chain tracing

Yes

No

Basic

Prod observability

Yes

Dev only

Yes

CI integration

With API

Native

Manual

Node.js support

SDK

CLI

Python-only

Why We Chose Langfuse for Everything
After testing Promptfoo locally and exploring Trulens for eval logic, we realized something:

We were already using Langfuse for logging — and it could do everything else too.

Why Langfuse Worked:
  • Native support for both manual and LLM-based evaluation

  • Centralized trace and prompt version management

  • Easy API for scoring and tagging

  • Excellent UI for exploring test sets, failures, and regressions

  • No context switching between tools

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

  • Prod scoring

  • Trace analytics

  • Prompt performance history

How We Use Langfuse Day-to-Day
Here’s how our evaluation workflow looks now:
  1. Define golden questions + expected answers

  2. Log test runs with prompt versions via Langfuse SDK

  3. Run automated evaluations (using GPT-4 to score)

  4. Review edge cases manually in the Langfuse UI

  5. Tag or revert poor-performing prompts

  6. Track performance over time across versions

We do this both:
  • In CI (dev runs with golden test sets)

  • In production (log and auto-score live interactions)

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

"Prompt engineering without scoring is like debugging without logs."

Mistakes to Avoid with LLM Testing
Tools that support platform engineering include

The platform team must treat these tools as backend dependencies of the platform—not just one-off choices.

Conclusion
  1. Treating it like traditional unit testing
    LLM outputs are fuzzy — use soft scoring, not pass/fail.

  2. Ignoring prod data

    Production reveals real-world edge cases. Logging and scoring live output is essential.

  3. Relying only on humans
    Manual reviews don’t scale. Use LLMs to assist, especially for scoring large sets.

  4. Fragmenting your tooling

    Try to consolidate logging, scoring, and traceability. It saves time and reduces blind spots.

Practical Benefits We Got
  • Version visibility — we can trace which version of a prompt caused what output

  • Test automation — we now run LLM tests in CI like code

  • Quality improvements — scoring forced us to quantify vague “good enough” prompts

  • Debuggable chains — full traces show what happened and why

Even non-technical teammates can now open Langfuse and inspect LLM outputs, tags, and evaluation scores.

Success Tips from Our Rollout
  • Start with a small golden dataset — 10–15 test questions are enough to begin

  • Write a clear evaluation prompt — tell GPT-4 what to look for and how to score

  • Tag your versions — helps with comparing results over time

  • Integrate early — log and score LLM output even if you’re not ready to act on it yet

  • Make it visible — treat prompt performance like app performance

Final Thoughts: One Stack, Full Loop

You don’t need five tools, a bunch of notebooks, and a manual checklist to manage LLM prompts.

We consolidated everything into one stack — Langfuse — and got a full prompt lifecycle: from testing and tuning to scoring and tracking.

If you're building anything serious on top of LLMs, set this up early. The effort pays off in stability, speed, and confidence.

Try this setup and stop flying blind.

FAQ

Q1: Can I use Langfuse with Node.js or LangChain.js?

A1: Yes, Langfuse provides SDKs and API integrations compatible with Node.js and LangChain.js apps.

Q2: Can I use LLMs to score LLM output?

A2: Yes. Langfuse supports LLM-as-a-judge evaluations where GPT-4 or Claude compares actual vs expected outputs.

Q3: Do I still need Promptfoo if I use Langfuse?

A3: No, unless you want a CLI-based local regression tool. Langfuse handles golden tests, scoring, and versioning.

Q4: What’s a golden test set?

A4: A collection of input questions and known-good answers used to benchmark prompt changes.

Q5: Can I trace multi-step chains with Langfuse?

A5: Yes. It’s especially useful for RAG pipelines, agent calls, or tool chains in LangChain.

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