RAG Evaluation · Pipeline Testing · QA

RAG Evaluation
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Q: What does RAG evaluation measure?

RAG (Retrieval-Augmented Generation) evaluation measures the full pipeline, not just the final answer. The key metrics are: (1) Retrieval Recall — did the retriever return the chunks that contain the answer? A low recall score means the LLM will never have the information it needs to answer correctly; (2) Context Precision — of all chunks retrieved, what fraction are actually relevant to the question? Low precision means the LLM's context window is filled with noise; (3) Answer Faithfulness — does the generated answer only claim things that are supported by the retrieved chunks? A faithfulness score below 0.8 indicates hallucination beyond the retrieved context; (4) Answer Relevance — is the answer actually addressing the question asked, or is it tangentially related? (5) Chunk Quality — are individual chunks semantically complete, or do they cut off mid-sentence or mid-concept? Poor chunking degrades all downstream metrics regardless of how good the LLM is.

Q: What are the most common RAG pipeline failures XPndAI finds?

The most common RAG failures we find in evaluation: (1) Chunk size too large or too small — oversized chunks dilute the relevant content; undersized chunks lose context; (2) Missing metadata filtering — the retriever returns chunks from the wrong document section, date range, or user-specific context; (3) Embedding model mismatch — the embedding model used for ingestion differs from query time, causing semantic drift; (4) No re-ranking — the top-k chunks by cosine similarity include irrelevant chunks that degrade answer quality; (5) Stale documents — the vector store contains outdated versions of documents, causing the LLM to answer from old information; (6) Multi-hop failures — questions requiring reasoning across two documents fail because the retriever can only retrieve chunks topically close to the question, not the intermediate reasoning step.

Your RAG pipeline retrieves chunks. The LLM generates an answer. But do the right chunks get retrieved? Does the answer actually match them? Does the model add facts not in the context? We evaluate every stage of your RAG pipeline — end to end.

Retrieval Recall Answer Faithfulness Context Precision Groundedness Chunk Quality Audit RAGAS Compatible

Evaluate My RAG Pipeline Pipeline Evaluation

RAG Evaluation — Free Scoping

Tell us your RAG stack (vector DB, embedding model, LLM) and biggest concern. We'll scope the evaluation immediately.

Pipeline Evaluation

Evaluate Every Stage — Not Just the Final Answer

Most RAG evaluations only check the final answer. We evaluate every stage — because a good final answer from bad retrieval is luck, not reliability.

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Document Ingestion

Chunk quality, overlap, metadata, format handling

Chunk completeness

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Embedding

Semantic accuracy, model fit for domain language

Embedding alignment

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Retrieval

Top-k recall, precision, re-ranking effectiveness

Retrieval recall

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Context Assembly

Context window fit, chunk ordering, noise ratio

Context precision

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Generation

Faithfulness to context, hallucination beyond docs

Faithfulness score

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Final Answer

Relevance, completeness, correctness vs ground truth

Answer accuracy

Evaluation Services

What the RAG Evaluation Covers

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Retrieval Recall Audit

For every test question in your benchmark: does the top-k retrieval actually include the chunk that contains the answer? A low recall score means even a perfect LLM will fail — the information never reaches it.

Recall@1, Recall@3, Recall@5, Recall@10
Hard questions that test multi-hop retrieval
Metadata filter accuracy (date, section, user)
Re-ranker effectiveness (if applicable)

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Faithfulness + Groundedness

Does the generated answer only assert claims that appear in the retrieved chunks? A faithfulness score below 0.8 means the LLM is adding facts from its parametric memory — hallucinating beyond the retrieved context.

Per-claim faithfulness scoring
Hallucination type: entity, number, date, relationship
Groundedness: every sentence traceable to a chunk?
Citation accuracy (if answer cites sources)

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Chunk Quality Audit

Bad chunking degrades every downstream metric regardless of LLM quality. We audit your chunking strategy for semantic completeness, optimal size, context overlap, and metadata richness.

Semantic completeness per chunk
Orphan chunks (incomplete sentences / concepts)
Optimal chunk size for your content type
Metadata schema richness (enables better filtering)

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End-to-End Benchmark

200–500 question-answer pairs with ground-truth answers drawn from your document corpus. Scored across all RAGAS metrics (faithfulness, answer relevance, context precision, context recall) plus custom domain accuracy.

200–500 Q&A pairs with ground truth
Adversarial questions (document boundaries, negation)
RAGAS-compatible scoring output
Custom domain metrics defined in scoping

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Latency + Cost Profiling

Production RAG systems face real latency and cost constraints. We profile retrieval latency, embedding latency, LLM latency, and token cost per query — and identify the bottleneck in your pipeline.

P50 / P95 / P99 latency per pipeline stage
Token cost per query (input + output + embedding)
Bottleneck identification
Cost reduction recommendations

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Optimization Recommendations

After evaluation: specific, prioritized fixes for your pipeline. Not generic advice — specific changes to chunk size, overlap, embedding model, re-ranker config, and prompt template, each with expected metric improvement.

Chunk size + overlap recommendation
Embedding model comparison for your domain
Re-ranker recommendation (cross-encoder vs LLM)
Prompt template improvements for faithfulness

Evaluation Cluster

Full AI Evaluation Stack

AI Agent EvaluationFull agent QA → LLM EvaluationHallucination + accuracy → Voice AI TestingSpeech + conversation → Document AIIDP pipelines → AI Agent DevelopmentBuild it first → Enterprise AIMulti-agent →

Pricing

RAG Evaluation Pricing

From quick retrieval audit to full end-to-end benchmark with optimization roadmap.

Retrieval Audit

₹1.5L – ₹4L

Retrieval + chunk quality only

Retrieval recall audit (Recall@1/3/5/10)
Chunk quality analysis
100-question test set
Specific fix recommendations
1–2 week delivery

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Common Questions

What does RAG evaluation measure?

RAG evaluation measures every stage of the pipeline: Retrieval Recall (did the right chunks get retrieved?), Context Precision (of chunks retrieved, what fraction are relevant?), Answer Faithfulness (does the answer only assert things supported by retrieved chunks?), Answer Relevance (does the answer address the question?), and Chunk Quality (are chunks semantically complete?). Most teams only check the final answer — but a correct-looking answer generated despite poor retrieval is luck, not reliability.

What are the most common RAG pipeline failures XPndAI finds?

Most common failures we find: (1) Chunk size wrong — too large dilutes relevance, too small loses context; (2) Missing metadata filtering — retriever ignores document source, date, or user context; (3) Embedding model mismatch between ingestion and query time; (4) No re-ranking — cosine similarity alone includes irrelevant chunks; (5) Stale documents — vector store has outdated versions; (6) Multi-hop failures — questions requiring reasoning across two documents fail because retriever can only find one.

Which RAG stacks can XPndAI evaluate?

We evaluate any RAG stack: vector DBs (Pinecone, Weaviate, Qdrant, ChromaDB, pgvector, Azure AI Search, Vertex AI Matching Engine), embedding models (OpenAI, Cohere, sentence-transformers, custom fine-tuned), LLMs (Claude, GPT-4, Gemini, Llama), and orchestration frameworks (LangChain, LlamaIndex, custom). We only need API access to your pipeline — we don't need your source documents or model weights.

RAG EvaluationServices