/05-rag-eval

name: rag-eval
description: >
  Use when the user has a RAG (Retrieval-Augmented Generation) system and wants to
  evaluate its quality — separating retrieval issues from generation issues. Also use
  when the user mentions RAG evaluation, faithfulness checking, hallucination detection
  in RAG, retrieval quality, chunking optimization, or "is my RAG pipeline working."
  Outputs a diagnostic matrix that pinpoints whether problems are in retrieval or generation.

RAG Eval

Evaluate RAG systems by diagnosing retrieval and generation separately. A single "RAG accuracy" number hides whether the problem is finding the right documents or using them correctly. This skill separates them so you know what to fix.

When to Activate

• User has a RAG pipeline (retriever + generator) with traces
• User wants to know if their RAG system hallucinates
• User is optimizing chunking strategy and needs before/after comparison
• User wants to build a RAG evaluation dataset

Checklist

You MUST create a task for each item and complete them in order:

1. **Load RAG traces** — validate query + context + answer triples 2. **Separate retrieval vs generation** — determine which layers to evaluate 3. **Run faithfulness evaluation** — is the answer grounded in retrieved docs? 4. **Run retrieval evaluation** — are the right documents retrieved? 5. **Build diagnostic matrix** — cross-tabulate to find root cause 6. **Output findings** — prioritized issues with concrete fixes

Fast path: run the bundled script

Once each trace has a faithfulness judgment (and ideally a retrieval signal), build the retrieval-vs-generation diagnostic matrix with the bundled, tested script (`scripts/rag_diagnostic.py`, standard library only, **no OpenJudge dependency**):

python scripts/rag_diagnostic.py --traces traces.jsonl

Trace rows: `{"faithful":bool}` or `{"faithfulness_score":1-5}` (>= 4 = faithful), plus an optional retrieval signal `{"retrieval_good":bool}` or `{"recall_at_k":0-1}` (>= 0.5 = good). It prints the generation faithful/hallucinating split, the 2×2 matrix when a retrieval signal is present, and the primary issue (retrieval vs generation). `--self-test` to verify it.

Steps below explain how to separate the layers and produce the faithfulness/retrieval signals (with OpenJudge graders or any judge).

Step 1: Load RAG Traces

The minimum data needed per trace:

# Each trace must contain:
trace = {
    "query": "What is the return policy?",
    # retrieved_docs are dicts with id + text (the id is required for retrieval
    # metrics like Recall@k; the text is required for the faithfulness check).
    "retrieved_docs": [
        {"id": "doc_1", "text": "Returns are accepted within 30 days..."},
        {"id": "doc_2", "text": "Refunds are issued to the original..."},
    ],
    "answer": "You can return items within 30 days for a full refund.",
    "reference_answer": "Our 30-day return policy allows full refunds.",  # optional
    "gold_doc_ids": ["doc_1", "doc_3"],  # optional — which docs should have been retrieved
}

If your traces only have raw strings (no ids), wrap them first so retrieval metrics work: `retrieved_docs = [{"id": f"d{i}", "text": t} for i, t in enumerate(raw_strings)]`.

Validate data completeness and report any gaps. If > 20% of traces are missing key fields, ask the user to confirm the schema before proceeding.

Step 2: Separate Retrieval vs Generation

RAG failures come from two independent sources:

| Source | What goes wrong | Metric to use | |--------|----------------|---------------| | **Retrieval** | Wrong/missing documents returned | Recall@k, Precision@k, MRR | | **Generation** | Model misuses or fabricates beyond docs | Faithfulness (HallucinationGrader) | | **Generation** | Answer doesn't address the query | Relevance (RelevanceGrader) |

Why separate them? A system with perfect retrieval but poor generation needs prompt engineering. A system with poor retrieval needs chunking/embedding work. Treating them as one problem wastes effort.

Step 3: Faithfulness Evaluation

Use OpenJudge `HallucinationGrader` to check if the answer stays grounded in retrieved documents:

from openjudge.graders.common.hallucination import HallucinationGrader
from openjudge.runner.grading_runner import GradingRunner

faithfulness_grader = HallucinationGrader(model=model)

runner = GradingRunner(
    grader_configs={"faithfulness": faithfulness_grader},
    max_concurrency=8,
)

# Dataset format for HallucinationGrader
dataset = [
    {
        "query": trace["query"],
        "response": trace["answer"],
        "context": "\n\n".join(doc["text"] for doc in trace["retrieved_docs"]),
    }
    for trace in traces
]

results = await runner.arun(dataset)

# HallucinationGrader scores 1-5 (5 = no hallucination, fully grounded)
# Binarize: score >= 4 → faithful, score < 4 → hallucination

Also evaluate **answer relevance** — does the response actually address the query?

from openjudge.graders.common.relevance import RelevanceGrader

relevance_grader = RelevanceGrader(model=model)

Step 4: Retrieval Evaluation

If gold_doc_ids are available, compute retrieval metrics:

def recall_at_k(retrieved_docs, gold_ids, k=5):
    """Fraction of gold docs found in top-k retrieved docs."""
    retrieved_ids = set(doc["id"] for doc in retrieved_docs[:k])
    gold_set = set(gold_ids)
    if not gold_set:
        return None
    return len(retrieved_ids & gold_set) / len(gold_set)

def precision_at_k(retrieved_docs, gold_ids, k=5):
    """Fraction of top-k docs that are relevant."""
    retrieved_ids = set(doc["id"] for doc in retrieved_docs[:k])
    gold_set = set(gold_ids)
    if not retrieved_ids:
        return 0
    return len(retrieved_ids & gold_set) / len(retrieved_ids)

def mrr(retrieved_docs, gold_ids):
    """Mean Reciprocal Rank — how early the first relevant doc appears."""
    for i, doc in enumerate(retrieved_docs):