Guardrails and RAG Evaluation
Block bad outputs and measure retrieval quality.
Guardrails and RAG Evaluation is a free MLOps Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
LLMs Need Bumpers
Left alone, a model can leak data or go off-topic. Guardrails are checks around the model that keep inputs and outputs inside safe bounds. 🛡️
Validate the Input First
Before calling the model, screen the user message for prompt injection or banned content. An input guard stops bad requests from ever reaching the LLM.
Check the Output Too
After generation, scan the answer for toxicity, secrets, or wrong format. An output guard blocks or rewrites unsafe text before the user sees it.
if contains_pii(answer):
answer = "Sorry, I cannot share that."Enforce Structure
When you need JSON, validate it against a schema and retry on failure. A schema guard turns flaky free text into reliable, parseable output.
from pydantic import BaseModel
class Reply(BaseModel):
answer: str
confidence: floatGuardrail Libraries
Tools like Guardrails AI and NeMo Guardrails bundle these checks. A library gives you ready-made validators instead of hand-rolling every rule.
Now Meet RAG
Many LLM apps fetch documents and feed them to the model. This RAG pattern, retrieval augmented generation, grounds answers in your own data.
Two Things Can Break
RAG can fail at retrieval or at generation. So you evaluate the retriever and the answer separately, because fixing one will not fix the other.
Score the Retrieval
Ask whether the fetched chunks actually contain the answer. Context recall measures if the right information made it into the prompt at all.
Score the Grounding
Even with good context, the model may invent details. Faithfulness checks that every claim in the answer is backed by the retrieved text.
Score the Relevance
Finally, judge whether the answer addresses the question asked. Answer relevance rounds out the picture beyond just being grounded.
Frameworks Like RAGAS
RAGAS computes faithfulness, context recall, and relevance from your cases. A framework turns fuzzy RAG quality into numbers you can track over time.
from ragas import evaluate
result = evaluate(dataset, metrics=[faithfulness, context_recall])Quick Check
Your RAG bot answers confidently but invents facts not in the documents. Which metric flags this?
Recap
You added guardrails on inputs and outputs and learned to evaluate RAG with faithfulness, context recall, and relevance. Safe and grounded. You did it! 🎉
Frequently asked questions
Is the “Guardrails and RAG Evaluation” lesson free?
Yes — the full text of “Guardrails and RAG Evaluation” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Guardrails and RAG Evaluation”?
Block bad outputs and measure retrieval quality. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Guardrails and RAG Evaluation” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- How LLMOps Differs from Classic MLOps
- Version Prompts and Evaluate Outputs
- Trace and Monitor LLM Calls
- Guardrails and RAG Evaluation