LLMs are impressive—until they confidently say something wrong.
If you’ve built a chatbot, a support assistant, a RAG search experience, or an “agent” that takes actions, you’ve already met the core problem: hallucinations. And the uncomfortable truth is: you won’t solve it with a single prompt tweak.
You solve it the same way you solve uptime or performance: with a reliability stack.
This guide explains a practical approach to LLM evaluation that product teams can actually run every week—without turning into a research lab.
TL;DR
- Hallucinations are not a rare edge case; they’re a predictable failure mode.
- The fix is not one trick—it’s a system: Test → Ground → Guardrail → Monitor.
- You need an evaluation dataset (“golden set”) and automated checks before shipping.
- RAG apps must evaluate retrieval quality and groundedness, not just “good answers”.
- Production monitoring is mandatory: regressions will happen.
Why LLMs hallucinate (quick explanation)
LLMs predict the next token based on patterns in training data. They’re optimized to be helpful and fluent, not to be strictly factual.
So when a user asks something ambiguous, something outside the model’s knowledge, something that requires exact policy wording, or something that depends on live data…the model may “fill in the blank” with plausible text.
Your job isn’t to demand perfection. Your job is to build systems where wrong outputs become rare, detectable, and low-impact.
The Reliability Stack (Test → Ground → Guardrail → Monitor)
1) TEST: Build automated LLM evaluation before you ship
Most teams “evaluate” by reading a few chats and saying “looks good.” That doesn’t scale.
Step 1: Create an eval dataset (your “golden set”)
Start with 50–100 real questions from your product or niche. Include:
- top user intents (what you see daily)
- high-risk intents (payments, security, health, legal)
- known failures (copy from logs)
- edge cases (missing info, conflicting context, weird phrasing)
Each test case should have: Input (prompt + context), Expected behavior, and a Scoring method.
Tip: Don’t force exact matching. Define behavior rules (must cite sources, must ask clarifying questions, must refuse when policy requires it, must call a tool instead of guessing).
Step 2: Use 3 scoring methods (don’t rely on only one)
A) Rule-based checks (fast, deterministic)
- “Must include citations”
- “Must not output personal data”
- “Must return valid JSON schema”
- “Must not claim certainty without evidence”
B) LLM-as-a-judge (good for nuance)
Use a judge prompt with a strict rubric to score: groundedness, completeness, and policy compliance.
C) Human review (calibration + high-risk)
- review a sample of passing outputs
- review all high-risk failures
- review new feature areas
Step 3: Run evals for every change (like CI)
Trigger your eval suite whenever you change the model, system prompt, retrieval settings, tools/function calling, safety filters, or routing logic. If scores regress beyond a threshold, block deploy.
2) GROUND: Force answers to be traceable (especially for RAG)
If correctness matters, the model must be grounded.
Grounding method A: RAG (docs / KB)
Common RAG failure modes: retrieval returns irrelevant docs, returns nothing, context is too long/noisy, docs are outdated.
What to do: require answers only using retrieved context, require citations (doc id/URL), and if context is weak: ask clarifying questions or refuse.
Grounding method B: Tools (APIs, DB queries)
If the answer depends on live facts (pricing, account, inventory), don’t let the model guess—fetch data via tools and then summarize.
Grounding method C: Constrained output formats
If the LLM outputs code/SQL/JSON/tool calls: validate schema, reject unsafe actions, and add a repair step for formatting errors.
3) GUARDRAILS: Reduce harm when the model is uncertain
Guardrails aren’t “restricting AI.” They’re risk management.
Guardrail A: “I don’t know” + escalation
A safe assistant should admit uncertainty and offer a next step (search sources, ask for details, escalate to a human).
Guardrail B: Mandatory citations in factual mode
If it can’t cite sources, it should not claim facts. Offer general guidance and label it clearly.
Guardrail C: Risk tiers by intent
- Low risk: drafting, brainstorming, rewriting
- Medium risk: troubleshooting, product policy
- High risk: legal/medical/payments/security
High risk needs stricter prompts, stronger grounding, and human handoff.
Guardrail D: Tool permissioning (for agents)
If an LLM can take actions: use allowlists, confirmations for destructive steps, rate limits, and audit logs.
4) MONITOR: Production observability (where real failures show up)
Even perfect test suites won’t catch everything. Your model will drift.
Minimum logging (do this early)
- prompt + system message version
- model name/version
- retrieved docs + scores (RAG)
- tool calls + parameters
- response
- user feedback
- latency + token cost
(Redact sensitive content in logs.)
Metrics that matter
- Grounded answer rate: % answers with citations in factual mode
- Escalation rate: how often the bot hands off
- User satisfaction: feedback + resolution rate
- Retrieval quality: % queries where top docs pass a relevance threshold
- Regression alerts: eval score drops after changes
LLM Evaluation Checklist (for teams)
- Offline: eval dataset (50–200), automated checks, regression thresholds, versioned prompts/configs
- Grounding: citations for factual mode, retrieval metrics, tool calls for live data
- Guardrails: intent tiers, refusal + escalation path, tool permissions
- Monitoring: logs with redaction, dashboards, regression alerts
FAQ
What is LLM evaluation?
LLM evaluation is the process of testing an AI model’s outputs against a rubric (accuracy, safety, groundedness, format) using automated checks and human review.
How do you reduce AI hallucinations?
You reduce hallucinations with a reliability stack: automated tests, grounding (RAG/tools/citations), guardrails (refusal/escalation), and production monitoring.
What is RAG evaluation?
RAG evaluation checks whether retrieval returns the right documents and whether the final answer is grounded in those documents using citation and correctness scoring.
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