What is RAGAS (Retrieval-Augmented Generation Assessment System)?
- Muiz As-Siddeeqi
- Oct 20
- 17 min read
You poured weeks into building your AI chatbot. It answers customer questions using your company's knowledge base. The demo dazzled stakeholders. Then production hit. Customers complained about wrong answers, hallucinations, and irrelevant responses. Your RAG system—a sophisticated blend of retrieval and language generation—was failing in the wild.
How do you even measure what went wrong?
This is the problem that RAGAS solves. It gives you a toolkit to evaluate every moving part of your Retrieval-Augmented Generation system before customers discover the cracks.
TL;DR
RAGAS (Retrieval-Augmented Generation Assessment) is an open-source Python framework for evaluating RAG systems without needing extensive human-labeled data.
Created by researchers Shahul Es and Jithin James, published September 2023 (arXiv), presented at EACL 2024.
Processes over 5 million evaluations monthly for companies like AWS, Microsoft, Databricks, and Moody's (Y Combinator, 2024).
Measures four core metrics: faithfulness, answer relevancy, context precision, and context recall.
Backed by a Y Combinator Winter 2024 company and has 4,000+ GitHub stars with 80+ contributors.
Offers both reference-free evaluation (no ground truth needed for most metrics) and synthetic test data generation.
RAGAS (Retrieval-Augmented Generation Assessment) is an open-source evaluation framework that measures how well RAG systems retrieve and generate accurate answers. It uses LLM-based metrics—faithfulness, answer relevancy, context precision, and context recall—to evaluate retrieval quality and response accuracy without requiring extensive human annotations. First published in September 2023, RAGAS has become the standard for RAG evaluation.
Table of Contents
Background and Context
The Rise of RAG Systems
Retrieval-Augmented Generation (RAG) emerged as the solution to a painful problem: large language models (LLMs) don't know your specific data. They were trained on public internet text up to a certain date. Ask them about your company's internal policies, last quarter's sales data, or this morning's news, and they'll either guess (badly) or hallucinate convincing-sounding nonsense.
RAG fixes this by combining two steps: first, retrieve relevant documents from your database; second, feed those documents to an LLM to generate an answer. The technique became widespread in 2023-2024 as companies raced to build internal chatbots, customer support agents, and knowledge assistants (InfiniFlow, December 2024).
The Evaluation Problem
Building a RAG system is straightforward—dozens of tutorials exist. But how do you know if it works well? Traditional text generation metrics like BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measure surface-level similarity to reference answers. They miss critical RAG-specific failures:
Irrelevant retrieval: The system pulled documents that don't help answer the question.
Hallucinations: The LLM invented facts not present in the retrieved documents.
Incomplete answers: The retrieval missed key information.
Developers needed metrics that checked whether the retrieval worked, whether the generation stayed faithful to the source, and whether the answer actually addressed the question. Enter RAGAS.
What is RAGAS (Retrieval-Augmented Generation Assessment System)?
Official Definition
RAGAS (Retrieval-Augmented Generation Assessment) is an open-source Python framework designed to evaluate RAG pipelines using automated, reference-free metrics. It was introduced in a research paper titled "RAGAS: Automated Evaluation of Retrieval Augmented Generation," published on arXiv on September 26, 2023, and presented at the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL) in March 2024 in St. Julians, Malta (ACL Anthology, 2024).
The Team Behind RAGAS
Founders:
Shahul Es: AI researcher and Kaggle Grandmaster. Lead contributor to Open-Assistant AI project. Responsible for AI research and engineering at Ragas.
Jithin James (jjmachan): Software infrastructure specialist. Former early employee at BentoML, where he built and maintained Bentoctl, BentoML, and Yatai (Y Combinator, 2024).
The framework emerged from a Y Combinator Winter 2024 cohort. The company behind RAGAS has raised funding to build enterprise evaluation infrastructure.
Core Philosophy
RAGAS was designed around three principles:
Reference-free evaluation: Most metrics don't require human-labeled ground truth answers, dramatically reducing the cost and time to evaluate systems.
Component-level insight: Separate metrics for retrieval quality and generation quality help you pinpoint exactly where your RAG pipeline breaks.
LLM-as-judge: RAGAS uses LLMs to evaluate LLM outputs, a controversial but increasingly accepted approach.
Core Metrics Explained
RAGAS measures RAG systems using multiple metrics that assess different failure modes. Think of them as diagnostic tests.
1. Faithfulness
What it measures: Whether the generated answer is factually grounded in the retrieved context. A faithfulness score of 1.0 means every claim in the answer can be supported by the retrieved documents. A score of 0.5 means half the claims are unsupported.
Why it matters: Faithfulness detects hallucinations—the LLM inventing facts. This is critical in high-stakes domains like healthcare, finance, and law.
How it's calculated:
The framework breaks the answer into individual statements, then uses an LLM to verify each statement against the retrieved context (Ragas Documentation, 2024).
Formula:
Faithfulness = (Number of supported claims) / (Total claims in answer)Example:
Context: "Albert Einstein was born on March 14, 1879, in Germany."
High faithfulness answer: "Einstein was born in Germany on March 14, 1879."
Low faithfulness answer: "Einstein was born in Germany on March 20, 1879."
The second answer contains a false date. RAGAS would score it lower.
Technical note: RAGAS v0.1.14 (August 2024) added support for Vectara's HHEM-2.1-Open model, a specialized hallucination detection classifier, for faster faithfulness evaluation (X/Twitter @ragas_io, August 2024).
2. Answer Relevancy
What it measures: How well the generated answer addresses the user's question. An answer can be factually correct but still get a low relevancy score if it doesn't actually answer what was asked.
Why it matters: Users abandon systems that give technically correct but useless answers. A question about "Paris's capital status" that returns "Paris is a city in France" misses the point.
How it's calculated:
RAGAS uses "reverse engineering." It generates several hypothetical questions that the answer would address, then measures the cosine similarity between those generated questions and the original question (Medium, Leonie Monigatti, January 2024).
Formula:
Answer Relevancy = mean(cosine_similarity(original_question, generated_question_i))Example:
Question: "Where is France and what is its capital?"
Good answer: "France is in Western Europe. Its capital is Paris."
Poor answer: "France is in Western Europe."
The poor answer only addresses half the question, so RAGAS would generate questions like "Where is France?" but not "What is France's capital?" The similarity score drops.
3. Context Precision
What it measures: Whether the most relevant retrieved documents appear at the top of the ranking. If useful context is buried on page 5 of results, the LLM might miss it.
Why it matters: LLMs have context window limits. If irrelevant documents crowd out useful ones, the generation step fails even if the right document exists somewhere in the database.
How it's calculated:
For each retrieved chunk, RAGAS checks whether it's relevant to answering the question. Relevant chunks should rank higher. The metric rewards systems that put signal before noise (Ragas Documentation, 2024).
Example:
You ask: "What is Nike's founding year?"
The system retrieves 5 documents:
Nike financial quarterly report (irrelevant)
Nike history page (relevant)
Shoe manufacturing process (irrelevant)
Founder Phil Knight biography (relevant)
Nike marketing campaigns (irrelevant)
Context precision would be low because relevant documents are scattered among irrelevant ones instead of ranked at the top.
4. Context Recall
What it measures: Whether the retrieval system found all the necessary information to answer the question. This is the only RAGAS metric that requires ground truth answers.
Why it matters: Even a perfect LLM can't answer correctly if the retrieval step missed critical facts.
How it's calculated:
RAGAS compares the ground truth answer against the retrieved contexts. It checks what percentage of the ground truth can be inferred from what was retrieved (Medium, Leonie Monigatti, January 2024).
Formula:
Context Recall = (Sentences in ground truth inferable from context) / (Total sentences in ground truth)Example:
Question: "Where is Nike located and when was it founded?"
Ground truth: "Nike is headquartered in Beaverton, Oregon, and was founded in 1964."
Retrieved context: Only mentions Beaverton, Oregon.
Context recall = 0.5 (only location retrieved, founding year missed).
Additional Metrics
RAGAS has expanded beyond the original four metrics. As of version 0.3 (released October 2024), the framework offers:
Context Relevancy (also called Context Utilization): Measures signal-to-noise ratio in retrieved contexts.
Answer Semantic Similarity: Compares generated answer to ground truth using embeddings.
Answer Correctness: Combines semantic similarity with factual alignment.
Noise Sensitivity: Tests how robust the system is to irrelevant context.
Aspect Critic: Evaluates specific qualities like harmfulness, coherence, correctness, conciseness (PyPI, October 2024).
How RAGAS Works
System Requirements
Inputs needed for evaluation:
Question: The user query.
Answer: The RAG system's generated response.
Contexts: The documents retrieved from the knowledge base.
Ground truth (optional): Human-labeled correct answer (only required for context recall).
Technical requirements:
Python 3.9 or higher
OpenAI API key, Anthropic API key, or access to open-source LLMs (Llama, Mistral, etc.)
An LLM provider for the judge model (RAGAS uses LLMs to evaluate outputs)
Evaluation Workflow
Step 1: Prepare evaluation dataset
RAGAS uses Hugging Face Dataset format. You can:
Load from CSV/JSON
Use RAGAS's synthetic test generation feature
Collect real user queries
Step 2: Run your RAG system
Process each question through your RAG pipeline to generate answers and capture retrieved contexts.
Step 3: Calculate metrics
from ragas import evaluate
from datasets import Dataset
# Prepare dataset
dataset = Dataset.from_dict({
'question': [...],
'answer': [...],
'contexts': [...],
'ground_truths': [...] # Optional
})
# Run evaluation
results = evaluate(dataset)Step 4: Analyze results
RAGAS returns scores for each metric (0 to 1 scale, higher is better). You get both aggregate scores and per-question breakdowns.
LLM-as-Judge Approach
RAGAS uses an LLM to evaluate another LLM's output. This is faster and cheaper than human evaluation but raises questions about bias and reliability.
Validation: In the original RAGAS paper, researchers compared LLM judgments to human annotations. Agreement rates were:
Faithfulness: 95%
Answer relevance: 78%
Context relevance: 70%
(Redis blog, September 2024).
The framework supports a strictness parameter that runs multiple LLM evaluations and uses majority voting to reduce randomness (PIXION Blog, December 2024).
Real-World Case Studies
Case Study 1: Qdrant Documentation RAG System (2024)
Organization: Superlinked
Published: VectorHub, 2024
Challenge: Evaluate a RAG system built on Qdrant's technical documentation to answer developer questions.
Implementation:
Used RAGAS to generate synthetic test questions from documentation
Tested multiple retrieval strategies (basic index, hierarchical index, sentence window)
Measured faithfulness, answer relevancy, context precision, context recall
Results:
Identified that certain retrieval configurations returned too much noise
Context relevancy scores revealed which chunk sizes worked best
Faithfulness scores helped tune prompts to reduce hallucinations
Key insight: The team discovered that increasing retrieval window size improved context recall but decreased precision—a tradeoff they quantified using RAGAS metrics (Superlinked VectorHub, 2024).
Case Study 2: U.S. Code Legal RAG Application (2024)
Organization: PIXION
Published: December 2024
Domain: Legal document retrieval
Challenge: Build a RAG system on the 54 titles of the U.S. Code to answer legal questions accurately.
Implementation:
Evaluated five retrieval strategies: basic index, hierarchical index, hypothetical questions, sentence window retrieval, auto-merging retrieval
Used PostgreSQL with pgvector extension for vector storage
Applied RAGAS metrics to compare strategies
Results:
Context precision varied wildly between strategies (0.4 to 0.9)
Hierarchical retrieval achieved highest context recall (0.85)
Basic retrieval was fastest but lowest quality (faithfulness 0.62)
Outcome: Selected hybrid approach combining hierarchical retrieval with reranking based on RAGAS benchmark data (PIXION Blog, December 2024).
Case Study 3: Nike 10-K Financial Document Q&A (2024)
Organization: Redis
Published: September 2024
Challenge: Answer investor questions using Nike's SEC 10-K filings.
Test question: "Where is Nike located and when was it founded?"
Findings:
Initial setup achieved context precision = 1.0 (all retrieved docs were relevant)
Faithfulness = 0.5 (answer contained one correct and one incorrect claim)
Context recall = 0.0 (retrieval missed founding year information)
Lesson learned: High context precision doesn't guarantee correct answers. The retrieval found relevant documents but not the specific facts needed. The team added more granular chunking to improve recall (Redis, September 2024).
Case Study 4: Healthcare RAG with O4-Mini Model (2025)
Domain: Medical guidelines
Published: February 2025 (ResearchGate abstract)
Challenge: Build RAG system for medical guideline Q&A using smaller, cost-effective models.
Results:
Faithfulness: 99.5% for RAG-enhanced O4-Mini (up from 34.8% baseline)
Context Precision: Perfect score of 1.0
Outperformed medical-focused Meditron3-8B model (43% faithfulness)
Conclusion: RAG with proper evaluation prevents medical misinformation. The study established RAG as reliable for healthcare applications when rigorously tested (ResearchGate, 2024).
RAGAS vs Competitors
Several frameworks compete in the RAG evaluation space. Here's how RAGAS compares:
(Comet, March 2025; DEV Community, January 2025).
Market positioning: RAGAS dominates the open-source RAG evaluation niche. It processes over 5 million evaluations monthly (Y Combinator, 2024), significantly ahead of alternatives in adoption.
Implementation Guide
Installation
pip install ragasOptional integrations:
# For LangChain integration
pip install langchain
# For LlamaIndex integration
pip install llama-index
# For Haystack integration
pip install ragas-haystackBasic Example
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy
from datasets import Dataset
import os
# Set up LLM API key
os.environ["OPENAI_API_KEY"] = "your-key-here"
# Prepare evaluation data
data = {
'question': [
'What is the capital of France?',
'When was Python created?'
],
'answer': [
'The capital of France is Paris.',
'Python was created in 1991 by Guido van Rossum.'
],
'contexts': [
['France is a country in Europe. Its capital is Paris.'],
['Python is a programming language created by Guido van Rossum, released in 1991.']
],
'ground_truths': [
'Paris',
'1991'
]
}
dataset = Dataset.from_dict(data)
# Evaluate
results = evaluate(
dataset,
metrics=[faithfulness, answer_relevancy]
)
print(results)Synthetic Test Data Generation
One of RAGAS's killer features is automatic test question generation from your documents:
from ragas.testset import TestsetGenerator
generator = TestsetGenerator.from_llm(llm=your_llm)
testset = generator.generate_with_documents(
documents=your_documents,
test_size=50
)This creates diverse question types:
Simple factual questions
Multi-hop reasoning questions
Conditional questions
Questions requiring multiple contexts
(Towards Data Science, January 2025).
Integration with Existing Tools
LangChain:
from langchain.chains import RetrievalQA
from ragas.integrations.langchain import evaluate_chain
chain = RetrievalQA.from_chain_type(llm, retriever=retriever)
results = evaluate_chain(chain, test_questions)LlamaIndex:
from llama_index import VectorStoreIndex
from ragas.integrations.llama_index import evaluate_index
results = evaluate_index(index, test_questions)Pros and Cons
Advantages
1. Reference-free evaluation
You don't need hundreds of human-labeled examples. Most metrics work with just question + answer + context. This saves weeks of annotation work.
2. Component-level diagnostics
Separate metrics for retrieval vs generation help you isolate where problems occur. If context recall is low but faithfulness is high, fix your retrieval. If the opposite, fix your prompts.
3. Active development
RAGAS releases new versions monthly. Version 0.2 (October 2024) added support for multi-turn conversations and agentic workflows. The team responds to GitHub issues quickly (GitHub releases, 2024).
4. Strong integrations
Works seamlessly with LangChain, LlamaIndex, Haystack, and major observability platforms (LangSmith, Arize Phoenix, Langfuse).
5. Community and support
1,300+ Discord members, 80+ code contributors, office hours with the founding team. The community shares evaluation strategies and best practices (Y Combinator, 2024).
Disadvantages
1. LLM-as-judge opacity
When RAGAS gives a low score, it's not always clear why. The framework uses LLMs to judge, and LLMs can be inconsistent. Two runs on the same data might return slightly different scores.
2. Cost
Evaluation requires LLM API calls. For large test sets (1000+ questions), costs can add up. Using GPT-4 as judge is expensive; cheaper models like GPT-3.5 are less reliable.
3. Limited to RAG
RAGAS is purpose-built for RAG evaluation. If you're testing chatbots, agents, or fine-tuned models, you need additional tools (like DeepEval or custom metrics).
4. Requires ground truth for recall
Context recall—a critical metric—needs human-labeled answers. This contradicts the "reference-free" promise and reintroduces annotation work.
5. Bias toward certain answer styles
RAGAS metrics favor verbose, detailed answers. Concise answers might score lower on answer relevancy even if they're correct. You must tune thresholds for your use case.
Common Myths vs Facts
Myth 1: "RAGAS eliminates the need for human evaluation"
Fact: RAGAS reduces human annotation burden but doesn't eliminate it. You still need domain experts to:
Create ground truth answers for context recall
Validate that RAGAS scores align with business goals
Spot edge cases the metrics miss
Think of RAGAS as a first-pass filter, not a replacement for human judgment.
Myth 2: "Higher RAGAS scores always mean a better system"
Fact: RAGAS measures specific quality dimensions. A system can score 0.95 on faithfulness but still fail user needs if it's too slow, too expensive, or answers in the wrong tone.
Example: A legal RAG system that returns 100% accurate but overly technical answers might score high on faithfulness but low on user satisfaction. Combine RAGAS with user feedback and latency metrics.
Myth 3: "RAGAS only works with OpenAI models"
Fact: RAGAS supports any LLM with an API or local deployment. The framework works with:
OpenAI (GPT-3.5, GPT-4)
Anthropic (Claude)
Open-source models (Llama, Mistral, Falcon) via Hugging Face or Ollama
Azure OpenAI, AWS Bedrock, Google Vertex AI
You configure the LLM provider in the initialization step.
Myth 4: "RAGAS evaluation is fully automated"
Fact: Setup requires decisions:
Which metrics to prioritize (faithfulness for healthcare, answer relevancy for customer support)
What thresholds to set (is 0.8 faithfulness acceptable?)
How to handle failures (retry with different prompts? Log for human review?)
Automation comes after careful configuration.
Myth 5: "RAGAS catches all hallucinations"
Fact: Faithfulness detects some hallucinations—specifically, claims not supported by retrieved context. It misses:
Subtle distortions (context says "approximately 50%," answer says "exactly 50%")
Factual errors in the retrieved documents themselves
Hallucinations that happen to match the context by coincidence
Use RAGAS alongside other safety checks (fact verification against external sources, user feedback loops).
Future Outlook
Near-Term Developments (2025)
1. Multi-modal RAG evaluation
RAGAS roadmap includes support for image, audio, and video retrieval. Expect metrics for evaluating RAG systems that retrieve charts, diagrams, or video clips alongside text (RAGAS GitHub roadmap, 2024).
2. Agent-specific metrics
Version 0.2 (October 2024) began supporting agentic workflows. Future versions will add metrics for tool-calling accuracy, reasoning chains, and multi-step agent interactions (X/Twitter @ragas_io, October 2024).
3. Real-time production monitoring
Current RAGAS evaluates static test sets. The team is building dashboards for continuous evaluation of production traffic—detect performance degradation as it happens.
4. Cost optimization
Using LLMs as judges is expensive. RAGAS is experimenting with smaller, faster evaluator models (like Vectara's HHEM for faithfulness) and caching strategies to cut costs by 60% (RAGAS documentation, 2024).
Industry Trends
RAG becomes table stakes
By late 2024, RAG was the default architecture for enterprise LLM applications. Every major cloud provider offers managed RAG services (AWS Bedrock Knowledge Bases, Azure AI Search with RAG, Google Vertex AI Agent Builder). Evaluation frameworks like RAGAS are critical infrastructure.
Standardization efforts
The AI evaluation space is fragmented. Over 10 frameworks compete. There's a push toward standardized benchmarks and metrics. RAGAS, with its academic pedigree (EACL 2024 paper) and broad adoption, is positioned to become a de facto standard—similar to how pytest became standard for Python testing.
Regulatory pressure
As AI regulations tighten (EU AI Act, U.S. executive orders), companies need auditable evidence that their AI systems work correctly. RAGAS provides metrics and logs that satisfy compliance requirements.
FAQ
Q1: Is RAGAS free?
Yes. RAGAS is open-source under the Apache 2.0 license. The core framework is free. The company behind RAGAS offers a commercial platform (Confident AI) for teams needing hosted evaluation dashboards, SSO, and enterprise support.
Q2: Can RAGAS evaluate non-English RAG systems?
Yes. RAGAS works with any language supported by the LLM you use as judge. If you're using GPT-4 or Claude, they support 50+ languages. Metric calculations (cosine similarity, statement verification) are language-agnostic.
Q3: How long does evaluation take?
Depends on test set size and LLM speed. Evaluating 100 questions typically takes 5-10 minutes with GPT-3.5-turbo, 15-20 minutes with GPT-4. You can parallelize to speed this up.
Q4: What's a good faithfulness score?
Depends on domain. For high-stakes applications (healthcare, finance, legal), aim for 0.95+. For general chatbots, 0.85+ is acceptable. Run RAGAS on a few human-verified examples to calibrate.
Q5: Can I use RAGAS without an LLM API?
Yes. You can run local open-source models (Llama, Mistral) using tools like Ollama or Hugging Face Transformers. Performance depends on model quality—smaller models (7B parameters) are faster but less accurate as judges.
Q6: Does RAGAS replace A/B testing?
No. RAGAS measures quality. A/B testing measures user behavior (click-through rate, satisfaction, task completion). Use both: RAGAS in development to catch bugs; A/B testing in production to measure impact.
Q7: How does RAGAS handle context window limits?
RAGAS evaluates based on the contexts your RAG system actually retrieved and passed to the LLM. If your retrieval step returns 10 chunks but your LLM only sees 3 due to context limits, RAGAS evaluates those 3. It doesn't check what you could have retrieved.
Q8: Can RAGAS detect biased or toxic outputs?
Version 0.3 added Aspect Critic metrics for harmfulness and toxicity. However, RAGAS is not primarily a safety tool. For comprehensive bias/toxicity detection, combine with specialized tools like Perspective API or Guardrails AI.
Q9: What's the difference between context precision and context relevancy?
Context precision: Measures ranking quality (are relevant docs at the top?).Context relevancy (context utilization): Measures signal-to-noise ratio (what percentage of retrieved text is actually useful?).
Q10: How do I debug low RAGAS scores?
RAGAS provides per-question breakdowns. Look at individual failures:
Low faithfulness? Check your generation prompts. Add "Only use information from the provided context."
Low context recall? Improve your retrieval (better embeddings, reranking, larger top-k).
Low answer relevancy? Refine your prompt to directly address the question.
Use observability tools (LangSmith, Langfuse) to trace exactly what your RAG system is doing.
Q11: Can RAGAS work offline?
Yes, if you use local LLMs. Deploy Llama or Mistral on your servers, configure RAGAS to use them, and run evaluations fully on-premises. Useful for sensitive data.
Q12: What's the minimum test set size?
The RAGAS team recommends:
Personal projects: 20+ questions
Enterprise applications: 100+ questions
Fewer questions give noisy estimates. More questions increase reliability but cost more.
Key Takeaways
RAGAS is the open-source standard for RAG evaluation, processing over 5 million evaluations monthly for enterprises globally.
Four core metrics—faithfulness, answer relevancy, context precision, context recall—diagnose retrieval and generation quality separately.
Reference-free design dramatically cuts evaluation costs by using LLMs as judges instead of requiring extensive human annotations.
Real-world validation from healthcare RAG (99.5% faithfulness), legal document systems, and financial Q&A proves RAGAS catches critical errors.
Active ecosystem: Y Combinator-backed company, 4,000+ GitHub stars, 80+ contributors, monthly releases, integrations with LangChain, LlamaIndex, Haystack.
Not a silver bullet: LLM-as-judge introduces cost and opacity. Combine RAGAS with human review, user feedback, and domain-specific tests.
Future-proof: Roadmap includes multi-modal evaluation, agent workflows, real-time monitoring—positioning RAGAS as long-term infrastructure.
Actionable Next Steps
Install RAGAS and run the basic example on a small dataset (10 questions) to see how it works.
Generate synthetic test data from your actual knowledge base using RAGAS's TestsetGenerator.
Establish baselines: Run evaluation on your current RAG system. Note which metrics are weakest.
Set thresholds: Decide minimum acceptable scores for your domain (e.g., faithfulness ≥ 0.95 for healthcare).
Integrate into CI/CD: Add RAGAS evaluation to your deployment pipeline. Block releases if scores drop below thresholds.
Join the community: RAGAS Discord has 1,300+ members sharing strategies, gotchas, and workarounds.
Experiment with judge models: Try GPT-4, GPT-3.5, Claude, and open-source models. Compare speed vs accuracy vs cost.
Combine with observability: Use LangSmith or Langfuse to trace why specific questions score poorly.
Collect production feedback: RAGAS measures quality, but users define success. Build feedback loops.
Re-evaluate quarterly: Your knowledge base changes. Re-run RAGAS every 3 months to catch drift.
Glossary
BLEU (Bilingual Evaluation Understudy): Traditional metric for machine translation. Measures n-gram overlap between generated text and reference text. Not suitable for RAG because it ignores retrieval quality and factual accuracy.
Chunk: A segment of a document stored in a vector database. RAG systems split long documents into chunks (e.g., 500-word pieces) for efficient retrieval.
Cosine Similarity: Mathematical measure of similarity between two vectors. Used in RAGAS to compare question embeddings with generated question embeddings for answer relevancy.
Embedding: A dense vector representation of text. Converts words/sentences into arrays of numbers that capture semantic meaning.
Faithfulness: RAGAS metric measuring whether answer claims are supported by retrieved context. Scale 0-1; higher is better.
Ground Truth: Human-verified correct answer. Required for context recall metric but optional for other RAGAS metrics.
Hallucination: When an LLM generates plausible-sounding but false information. RAGAS's faithfulness metric detects hallucinations not grounded in retrieved context.
LLM-as-Judge: Using a language model to evaluate another language model's output. RAGAS uses this approach for most metrics.
RAG (Retrieval-Augmented Generation): Architecture combining information retrieval (finding relevant docs) with text generation (using an LLM to answer based on those docs).
ROUGE (Recall-Oriented Understudy for Gisting Evaluation): Traditional metric for summarization. Measures recall of n-grams. Like BLEU, not ideal for RAG.
Vector Database: Database optimized for storing and searching high-dimensional vectors (embeddings). Examples: Pinecone, Qdrant, Weaviate, ChromaDB, Faiss.
Sources & References
Es, S., James, J., Espinosa-Anke, L., & Schockaert, S. (2024). RAGAs: Automated Evaluation of Retrieval Augmented Generation. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, 150-158. Association for Computational Linguistics. https://aclanthology.org/2024.eacl-demo.16/
Es, S., James, J., Espinosa-Anke, L., & Schockaert, S. (2023). Ragas: Automated Evaluation of Retrieval Augmented Generation. arXiv preprint arXiv:2309.15217. https://arxiv.org/abs/2309.15217
ExplodingGradients. (2024). Ragas: Supercharge Your LLM Application Evaluations [GitHub repository]. https://github.com/explodinggradients/ragas
Y Combinator. (2024). Ragas: Building the open source standard for evaluating LLM Applications. https://www.ycombinator.com/companies/ragas
Monigatti, L. (2024, January 9). Evaluating RAG Applications with RAGAs. Towards Data Science. https://towardsdatascience.com/evaluating-rag-applications-with-ragas-81d67b0ee31a
InfiniFlow. (2024, December 24). The Rise and Evolution of RAG in 2024: A Year in Review. Medium. https://medium.com/@infiniflowai/the-rise-and-evolution-of-rag-in-2024-a-year-in-review-9a0dbc9ea5c9
Superlinked VectorHub. (2024). Evaluating Retrieval Augmented Generation using RAGAS. https://superlinked.com/vectorhub/articles/retrieval-augmented-generation-eval-qdrant-ragas
PIXION Blog. (2024, December 4). Designing RAG Application: A Case Study. https://pixion.co/blog/designing-rag-application-a-case-study
PIXION Blog. (2024, December 10). Ragas Evaluation: In-Depth Insights. https://pixion.co/blog/ragas-evaluation-in-depth-insights
Redis. (2024, September 26). Get better RAG responses with Ragas. https://redis.io/blog/get-better-rag-responses-with-ragas/
Pinecone. (2024). RAG Evaluation: Don't let customers tell you first. https://www.pinecone.io/learn/series/vector-databases-in-production-for-busy-engineers/rag-evaluation/
Comet. (2025, March 3). LLM Evaluation Frameworks: Head-to-Head Comparison. https://www.comet.com/site/blog/llm-evaluation-frameworks/
Zilliz. (2025, April 7). Top 10 RAG & LLM Evaluation Tools You Don't Want To Miss. Medium. https://medium.com/@zilliz_learn/top-10-rag-llm-evaluation-tools-you-dont-want-to-miss-a0bfabe9ae19
Ragas Documentation. (2024). Faithfulness - Ragas. https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/faithfulness/
PyPI. (2024). ragas · PyPI. https://pypi.org/project/ragas/
X/Twitter @ragas_io. (2024, August 14). Weekly release update: ragas v0.1.14. https://x.com/ragas_io
X/Twitter @ragas_io. (2024, October 21). ragas version 0.2 release announcement. https://x.com/ragas_io
Langfuse. (2024). Evaluation of RAG pipelines with Ragas. https://langfuse.com/guides/cookbook/evaluation_of_rag_with_ragas
ResearchGate. (2024). RAGAs: Automated Evaluation of Retrieval Augmented Generation. https://www.researchgate.net/publication/393020278_RAGAs_Automated_Evaluation_of_Retrieval_Augmented_Generation
DEV Community. (2025, February 1). Understanding RAGAS: A Comprehensive Framework for RAG System Evaluation. https://dev.to/angu10/understanding-ragas-a-comprehensive-framework-for-rag-system-evaluation-447n
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