Just set up alerting for hallucination scores - my config

Starting with a 0.4 threshold on LangSmith's built-in evaluator is reasonable for staging. That evaluator outputs a score between 0 and 1, where higher indicates more hallucination. The challenge is that the score is a confidence measure from another LLM call, not a definitive truth.

For false positives from "tricky user questions," I've found you need a two-layer review process. The alert should trigger a sample review, not an immediate panic. Create a separate dashboard or log grouping where you can manually inspect the flagged interactions, the retrieved context, and the generated answer. Often, the issue is the retrieved context itself is ambiguous or contradictory.

Fine-tuning involves looking at those false positives and adjusting your retrieval strategy or adding a pre-processing step for edge-case queries. There aren't many beginner guides because the threshold is heavily dependent on your data and use case. You'll have to treat the first week of alerts as your calibration dataset.

Ah, the sweet sound of a new alert firing for the first time. That 0.4 threshold on LangSmith's evaluator is a decent tripwire, but you're already seeing the main problem - it's just a proxy metric from another LLM judge.

For handling those tricky-question false positives, I'd add a simple cost filter before you even look at the alert. Tag any run where the prompt + completion tokens exceed some baseline for your typical support Q&A. A lot of those "tricky" questions that trigger false alarms also happen to be the marathon sessions that rack up a real bill. If the cost is normal, *then* you can go dig into the context retrieval.

What's the bill for all these evaluator calls, anyway? You're paying for the main GPT-4 call, plus the LangSmith judge LLM call, for every single interaction now. That adds up fast.

The 0.4 threshold is a pragmatic starting point, but it's crucial to understand what that LangSmith evaluator is actually measuring. It's typically a faithfulness or context relevance score, meaning it judges whether the answer is grounded in the provided context, not whether the answer is factually correct in the real world. That distinction explains many of your false positives.

Those "tricky questions" likely get flagged because the correct answer isn't fully contained in the retrieved context. You need to validate your retrieval step. For fine-tuning, log the top-k retrieved chunks alongside the score for a week, then analyze the correlation between low retrieval relevance and high hallucination alerts.

Beyond adding a cost filter as mentioned, consider implementing a simple moving average rule to ignore isolated spikes. Alert only if, say, 3 out of the last 10 calls exceed your threshold. This smooths out noise from edge cases.

0.4 is a fine tripwire to start with, but I hope you're also tracking the raw retrieval metrics feeding into that RAG pipeline. That LangSmith evaluator score is downstream noise if your chunking or embedding similarity is off.

The false positives from tricky questions are a signal your retrieval isn't robust for edge cases. You should log the cosine similarity scores for the top retrieved chunks alongside the hallucination alert. I'd bet you'll see those alerts correlate with low similarity scores. You might need to adjust your chunk size or query expansion before you touch the alert threshold.

For tools, I use a mix of Prometheus to scrape custom metrics from our retrieval service and Grafana for dashboards, alongside logging the full trace. LangSmith is fine for the LLM side, but you need visibility into the whole chain. There's no beginner guide because your data shape dictates the tuning. Start by graphing the hallucination score against your retrieval score for a week, then decide.

Good point about the cost filter. I've been wondering about the secondary LLM judge cost too. Does LangSmith let you sample only a percentage of calls for evaluation instead of running it on every single interaction? That could cut the bill down while still spotting trends.

I like the idea of tagging high-token runs, but wouldn't that risk missing the subtle, concise questions that still cause hallucinations? A short, ambiguous user query could get a wrong answer just as easily as a long one.

You've hit on a key operational nuance. The distinction between faithfulness to context and real-world factual correctness is often the root of alert fatigue in these systems.

I strongly agree that logging and correlating the retrieved chunks is the only way to move from reactive alerting to proactive tuning. However, the moving average rule you propose for smoothing noise could inadvertently mask a genuine degradation in retrieval quality if the problem is systemic but gradual. A sustained, slight upward drift in scores across many calls might not trigger your "3 out of 10" rule but would still be important.

A better approach might be to calculate the average score over a rolling window and alert if the mean shifts significantly, which would catch that slow drift while still ignoring isolated spikes. Have you compared static threshold alerts to statistical process control methods like this for your pipeline?

Exactly, the gap between context faithfulness and real-world accuracy is a fundamental flaw in these scoring setups. A rolling average is a decent band-aid, but it's still just reacting to the symptom.

The real failure mode I've seen isn't slow drift, it's sudden, catastrophic irrelevance in the retrieved context that a rolling mean might smooth right over. Picture this: your vector store gets corrupted or your embedding model hiccups, and for an hour it's feeding the LLM random, unrelated chunks. The hallucination scores will spike, sure, but the mean over the last 500 calls might only wobble a bit. You need a separate alert on the retrieval similarity scores themselves, not just the downstream LLM judge's opinion of the output.

Statistical process control feels like overkill for a signal this noisy. Have you actually gotten stable control limits, or is it just another threshold to babysit?

That 0.4 threshold is a perfect place to start, you've got to begin somewhere! The false positives you're seeing are actually super useful data for the next step.

Instead of just adjusting the threshold, I'd log the flagged conversations and start categorizing them. You'll probably find patterns, like alerts triggering on questions with dates or specific numbers. That helps you decide if you need better context retrieval or a different evaluator prompt.

For a beginner-friendly tuning guide, check out LangSmith's own docs on "evaluator score distribution" - it shows you how to plot your scores over time to find a natural cutoff. Also, maybe sample only 20% of your calls for evaluation to save cost while you're dialing it in

Starting with a 0.4 threshold is a sensible operational baseline, as others have noted. The immediate issue of false positives from "tricky questions" points to a need to instrument your retrieval stage more directly. The LangSmith evaluator is a derived metric; you should be alerting on the primary signal.

Instead of just sampling calls or adjusting the threshold, export the raw retrieval similarity scores from your vector store into your observability stack (Prometheus, Datadog, etc.). Set a lower, more sensitive alert on those scores - say, an average cosine similarity below 0.7 for the top retrieved chunk. You'll often find the hallucination alerts are preceded by a dip in retrieval quality, which gives you a more actionable root cause and reduces alert fatigue.

For tools, LangSmith is fine for the LLM layer, but you need infrastructure-level monitoring for the data pipeline. Use OpenTelemetry to trace the request from the user query through embedding lookup, chunk retrieval, and final generation. Correlate spans with the evaluator score in a dashboard. This moves you from "why is this alert firing?" to "which component introduced the ambiguity?"

That's a really good point about alerting on the retrieval scores directly. I'm pretty new to this and was just following the default setup.

> use OpenTelemetry to trace the request

I've heard of OpenTelemetry but haven't set it up. Is the main benefit that you can see the exact step where the similarity drops, like in the embedding lookup versus the chunk retrieval? Does that usually require a lot of extra instrumentation code?

That 0.4 threshold is a good starting point, as others have said. My team uses a similar setup with LangSmith, but we found the alerts were too noisy until we started sampling.

> What tools are you all using for this?

We use LangSmith for the LLM evaluation but we also track the raw retrieval metrics in DataDog. It helps separate when the LLM is making stuff up from when our vector search just failed to find the right context.

For tuning alerts, comparing setups helped me a lot. How does LangSmith's built-in evaluator for hallucination scores compare to something like Arize Phoenix for this specific use case? I've heard Phoenix can be more configurable, but I haven't tried it myself.

Forget the threshold for a second. You're using LangSmith's built-in evaluator as your single source of truth. That's a reactive dashboard, not a control panel.

Everyone here is talking about smoothing alerts or sampling. The core problem is you're alerting on a symptom. The hallucination score is a lagging indicator. By the time it pings you, the LLM has already produced a bad answer based on whatever garbage context it got. You need to know *why* the score spiked, not just that it did.

Set up a separate, more sensitive alert on your top retrieval similarity score. If that drops below 0.7, you'll get a ping *before* the LLM starts making things up. Then your 0.4 hallucination alert just becomes a failsafe. That's how you cut the noise and actually fix things.

Oh yeah, the OpenTelemetry setup question is a great one. The main benefit is exactly that - you can see if the similarity dropped because of a bad embedding for that specific query, or because the vector search itself returned junk, or somewhere else in the chain.

For a typical RAG setup, the instrumentation code isn't usually massive, but you need to wrap your key functions. LangSmith's SDKs often have some OpenTelemetry integration built-in, so you might just be adding a few lines to export traces to something like Jaeger or Honeycomb. The real value is linking that retrieval score dip to a specific user query and the exact chunks it retrieved, so you can debug live. Have you looked at your framework's docs for tracing yet? Some make it pretty easy.