The Only LLM Evaluation Stack You’ll Need: How We Standardized Testing, Scoring & Tracing 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.
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.
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.
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.
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."
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.
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 |
We were already using Langfuse for logging — and it could do everything else too.
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
Dev prompt testing
Prod scoring
Trace analytics
Prompt performance history
Define golden questions + expected answers
Log test runs with prompt versions via Langfuse SDK
Run automated evaluations (using GPT-4 to score)
Review edge cases manually in the Langfuse UI
Tag or revert poor-performing prompts
Track performance over time across versions
In CI (dev runs with golden test sets)
In production (log and auto-score live interactions)
"Prompt engineering without scoring is like debugging without logs."
The platform team must treat these tools as backend dependencies of the platform—not just one-off choices.
Treating it like traditional unit testing
LLM outputs are fuzzy — use soft scoring, not pass/fail.Ignoring prod data
Production reveals real-world edge cases. Logging and scoring live output is essential.
Relying only on humans
Manual reviews don’t scale. Use LLMs to assist, especially for scoring large sets.Fragmenting your tooling
Try to consolidate logging, scoring, and traceability. It saves time and reduces blind spots.
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.
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
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.
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.