How can we catch hallucinations in a RAG assistant before they reach customers in production?

Most teams only realize their RAG assistant is hallucinating after a customer screenshots a bad answer and sends it to support. By then, the damage is done: lost trust, incorrect actions taken, and a debug fire drill across logs and traces. If you want a RAG assistant you can actually put in front of customers, you need a way to catch hallucinations before they ever reach the UI or downstream tools.

Quick Answer: You catch hallucinations in a RAG assistant by turning offline evaluation into always-on guardrails: build high-quality hallucination evaluators on real traces, distill them into low-latency models, and run them in-line in production to intercept, block, or override unsafe outputs in under 200ms.

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The Quick Overview

• What It Is: A production-grade workflow for detecting and preventing hallucinations in RAG systems by combining evaluation, observability, and real-time guardrails.
• Who It Is For: Teams shipping customer-facing RAG assistants and agents—especially in regulated or high-stakes domains—who can’t afford to “hope” their model doesn’t hallucinate under load.
• Core Problem Solved: RAG assistants confidently fabricate answers even when they retrieve the wrong context—or no context at all—and most teams only see these failures after customers do.

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How It Works

At a high level, catching hallucinations before they reach customers requires three things:

1. A reliable way to measure hallucinations on realistic RAG traffic.
2. A mechanism to generalize that measurement into a reusable evaluator.
3. An always-on firewall that uses those evaluators to intercept unsafe responses in real time.

Galileo’s platform wires these steps into a single eval-to-guardrail loop:

1. Evaluate: Use Galileo’s Evaluation Engine on synthetic, dev, and early production traces to score hallucinations with 20+ out-of-the-box evaluators plus domain-specific, custom evaluators.
2. Signals: Continuously analyze 100% of production traces to uncover new hallucination patterns (unknown unknowns) and convert them into new evaluators.
3. Protect: Deploy those evaluators as real-time guardrails powered by Luna-2, intercepting responses in < 200ms and triggering block/redact/override/webhook actions before users see the failure.

1. Evaluate: Build hallucination detectors that match your RAG, not a benchmark

Generic “hallucination score” metrics miss the messy reality of your domain. In Galileo, you start by:

• Instrumenting your RAG
  Log sessions → traces → spans for each request:

  • Input query
  • Retrieved documents / chunks
  • Model response
  • Tool calls (if any)
  • Latency and cost per span

• Running baseline evaluators
  Use out-of-the-box RAG evaluators to get immediate signal on:

  • Context grounding: Does the answer stay within retrieved documents?
  • Citation correctness: Are referenced sources actually relevant?
  • Answer completeness: Does the model ignore key retrieved evidence?
  • Contradictions: Does the answer conflict with the context?

• Capturing ground truth and SME feedback
  For high-value flows (e.g., account changes, medical guidance, legal interpretations), have subject matter experts:

  • Label outputs as grounded, partially grounded, or hallucinated
  • Annotate why they’re hallucinated (missing context, invented entity, wrong number, etc.)

These labeled traces become your living evaluation asset—a test bed that reflects how hallucinations actually show up in your RAG, not just synthetic test prompts.

2. Signals: Find new hallucination patterns from live production traffic

No matter how good your pre-production tests, edge cases will slip through. Galileo’s Signals closes that gap by:

• Scanning 100% of production traces
  Not just sampled logs—every session, every span.
• Auto-clustering anomalous behavior
  Grouping traces where:
  • Answers disagree with known facts or previous system behavior
  • The model changes tone/style in ways that hint at prompt injection pressure
  • Tool calls keep failing or returning inconsistent states
• Surfacing hallucination patterns as “signals”
  For example:
  • “Answers about SKU-level pricing often cite non-existent discounts”
  • “Multi-step refund flows sometimes invert the refund amount”
  • “RAG assistant fabricates policy details when retrieval returns no relevant docs”

From there, you can generate an evaluator from a signal:
Describe the failure mode in natural language and Galileo will generate an LLM-as-judge-style evaluator, which you can then calibrate with few-shot examples from your labeled traces.

3. Protect: Turn hallucination evaluation into a real-time firewall

Detection without prevention just moves your latency from “user sees issue” to “engineer sees issue.” The point is to stop hallucinations before they ship.

Galileo Protect runs your hallucination evaluators inline, powered by compact Luna / Luna-2 models:

• Sub-200ms guardrailing
  Evaluates each model response (and optionally, retrieved context) under your hallucination evaluators.
• Deterministic actions, not passive scores
  For each guardrail metric, you define policies like:
  • If hallucination score > threshold → block the answer and return a safe fallback
  • If missing or low-confidence context → override with “I don’t know” plus a link to human support
  • If partial hallucination (e.g., some entities suspect) → redact the risky parts or escalate to a human via webhook
• Versioning and rollbacks
  Guardrail policies are versioned:
  • Test new thresholds on shadow traffic
  • Roll back to a known-safe policy without redeploying app code
  • A/B test stricter vs. looser hallucination thresholds per audience or feature

This turns hallucination evaluators into a real-time hallucination firewall sitting between your model and your user interface or tool execution layer.

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Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Evaluation Engine for RAG Hallucinations	Scores responses against retrieved context using 20+ out-of-the-box evaluators plus custom, domain-tuned evaluators.	Quantifies hallucination risk and grounding quality across your RAG stack, not just in lab prompts.
Signals for Unknown Hallucination Patterns	Analyzes 100% of production traces to surface new failure modes and auto-generate evaluators from them.	Finds hallucinations you weren’t explicitly looking for, turning “unknown unknowns” into governed behaviors.
Protect Real-Time Firewall (Luna-2)	Runs distilled evaluators via Luna-2 to intercept responses in < 200ms and trigger block/redact/override/webhook actions.	Stops hallucinations before customers or downstream tools see them, without blowing your latency or cost budget.

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Ideal Use Cases

• Best for customer-facing RAG assistants in regulated domains:
  Because it lets you enforce strict hallucination thresholds, block unsafe answers, and keep an auditable trail of interventions—critical for finance, healthcare, and legal workflows.

• Best for high-volume support and knowledge-base assistants:
  Because Galileo can evaluate and guardrail 100% of traffic at a fraction of heavyweight LLM-judge costs, using Luna-2 to achieve up to 97% lower monitoring cost with sub-200ms latency.

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Limitations & Considerations

• You still need good retrieval.
  Even the best hallucination firewall can’t fix a RAG system that rarely retrieves relevant documents. You’ll get value fastest if you’ve already invested in solid chunking, indexing, and retrieval strategies—then use Galileo’s RAG evaluators to validate and tighten them.

• Evaluator quality depends on your domain signal.
  Out-of-the-box evaluators get you started, but the highest precision on hallucinations comes from domain-specific tuning: SME annotations, carefully chosen few-shot examples, and CLHF-style refinement. Plan for a short calibration loop, not a one-click install.

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Pricing & Plans

Teams typically adopt Galileo along a path: start with evaluation on a slice of traffic, then expand to always-on supervision and protection for high-value assistants and agent flows.

While exact pricing depends on volume, deployment model (SaaS, VPC, on-prem), and integration depth, Galileo is designed to keep evaluation and guardrailing costs dramatically lower than running a general-purpose LLM as a judge on live traffic.

• Evaluate & Signals Plan: Best for teams needing to stand up robust hallucination measurement and RAG analytics, run experiments on prompts/models, and analyze 5,000+ traces per month before rolling into full production guardrails.

• Protect Plan: Best for teams needing real-time hallucination and threat firewalls on customer-facing assistants, with sub-200ms guardrailing, 100% traffic coverage, and enterprise deployment options (including SOC 2 Type II / HIPAA-ready setups).

For detailed pricing and deployment options—including VPC and on-prem—talk directly with the Galileo team.

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Frequently Asked Questions

How is this different from just using an LLM-as-judge to score hallucinations?

Short Answer: LLM-as-judge is useful for offline experiments, but it’s too slow and expensive to run on 100% of production traffic. Galileo distills those evaluators into Luna-2 so you can run them continuously as guardrails.

Details:
Teams often start with a heavyweight LLM (like GPT-4) to judge whether an answer is grounded in context. That’s fine for small test sets, but it breaks in production:

• Latency blows past your budget.
• Per-call costs make 100% coverage unrealistic.
• You get a score, but no consistent interception action wired into your app.

Galileo’s workflow:

1. Use LLM-as-judge during evaluation to prototype a hallucination evaluator.
2. Refine it with SME labels, few-shot examples, and CLHF.
3. Distill it into a compact Luna / Luna-2 model tuned for this evaluation task.
4. Serve that evaluator in Protect, giving you:
   • Sub-200ms latency
   • 97% lower monitoring cost
   • Direct integration with block/redact/override/webhook policies

So instead of an eval you can only afford in a notebook, you get a guardrail you can run on every request.

Can I use Galileo if my RAG assistant already has some basic checks and logging?

Short Answer: Yes. Galileo plugs into your existing traces and adds structured evaluation, proactive detection, and real-time guardrails on top of what you already log.

Details:
Most teams have:

• Logs or traces in an observability tool
• A few regex-based filters or feature flags for obvious bad content
• Some offline tests on synthetic prompts

The gaps:

• Those checks don’t understand grounding relative to retrieved docs.
• Logging is reactive—you find issues only after you know what to search for.
• Guardrails are brittle and hard-coded; changing them means re-deploying code.

With Galileo:

• You ingest traces from your existing stack (including tool calls and retrieval spans).
• You run structured RAG hallucination evaluators across that traffic.
• Signals surfaces new failure modes that your regexes and dashboards would never spell out.
• Protect becomes your central guardrail layer, with:
  • Versioned policies and thresholds
  • Audit logs for every interception
  • The ability to quickly roll out new protections as new hallucination patterns appear

You keep your existing logging and metrics—but hallucination detection and prevention move from “best-effort scripts” to a governed, versioned system.

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Summary

Catching hallucinations in a RAG assistant before they reach customers is not about a single clever prompt or a dashboard; it’s about wiring evaluation directly into production as a control plane. You:

1. Evaluate hallucinations against real RAG traces, using domain-tuned evaluators instead of generic benchmarks.
2. Use Signals to turn live incidents and anomalies into new evaluators, so your coverage improves with every failure you see.
3. Deploy Protect as a real-time firewall powered by Luna-2, so those evaluators can block, redact, or override hallucinated answers in under 200ms across 100% of traffic.

That’s the difference between a RAG demo that “usually works” and a RAG assistant you can safely put in front of customers.

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