RAG Evaluation Tools: Weights & Biases vs Ragas vs DeepEval vs TruLens
Failures in Retrieval Augmented Generation systems occur not only because of hallucinations but more critically because of retrieval poisoning. In such cases, the retriever returns documents that share substantial lexical overlap with the query but do not contain the necessary information.
We conducted a comparative analysis of six widely used evaluation tools, including Weights & Biases, Ragas, DeepEval, TruLens, and UpTrain. The goal was to determine their ability to distinguish genuinely relevant contexts from deliberately constructed deceptive negatives.
RAG evaluation tools benchmark results
Metrics explained
- Top-1 Accuracy: Given three contexts (Actual Answer, Hard Negative, Irrelevant), can the tool assign the highest relevance score to the Actual Answer? This measures safety against adversarial retrieval.
- NDCG@3 (Normalized discounted cumulative gain): Measures the quality of the ranking. Did the tool correctly order the contexts from most relevant to least relevant?
- Hard Negative: A context generated to look semantically identical to the query (same entities, same topic) but logically void of the actual answer.
Key findings
- LLM-based judges dominate: All tools using GPT-4o achieved >80% accuracy, with Weights & Biases and Ragas reaching near perfection. Reasoning capabilities are essential for detecting semantic traps.
- Framework overhead matters: Despite using the same underlying model (GPT-4o), DeepEval (82%) and UpTrain (89%) scored lower than Weights & Biases’ ContextRelevancyScorer (100%). Complex reasoning chains in prompts (Chain-of-Thought) can sometimes lead to over-analysis and “false positives” in simple relevance tasks, whereas Weights & Biases’ raw prompting approach maximized GPT-4o’s intrinsic capabilities.
RAG evaluation tools benchmark methodology
To benchmark the true reasoning capabilities of RAG monitoring tools, relying on random, unrelated documents (Easy Negatives) is insufficient. Modern vector databases rarely retrieve completely irrelevant data; they retrieve data that looks relevant but is factually incorrect or insufficient.
To simulate this, we constructed a controlled adversarial dataset. Each test case consists of a triplet:
- Question: The user query.
- Golden context: Contains the exact answer and supporting evidence.
- Hard negative (The trap): A context generated to be semantically highly similar to the question but logically void of the actual answer.
The multi-hop trap (Relation confusion)
Questions often require tracing a relationship chain (e.g., A is related to B, who is related to C). Hard negatives answer a simpler version of the question, breaking the chain.
Question ID 89: “Who publishes the game series that Retro City Rampage is a parody of?”
Target Answer: Rockstar Games
The entity distractor trap
Retrievers often find the correct location or subject, but return metadata about the wrong event or attribute.
Question ID 90: “…The Bridge Inn is the venue for which annual competition for telling lies, held in Cumbria, England?”
Target Answer: World’s Biggest Liar
Dataset construction
One major risk in LLM benchmarking is “Self-Preference Bias,” where an LLM evaluator (e.g., GPT-4o) prefers text generated by itself.
To eliminate this bias, we used a Cross-Model Generation protocol:
- Base Dataset: 100 question-answer pairs derived from the HaluEval benchmark (General Knowledge).
- Adversarial Generator: We utilized Anthropic Claude 4.5 Sonnet to generate the “Hard Negative” contexts.
- The Judge: We utilized OpenAI GPT-4o as the evaluator engine for the tools.
By using Claude for generation and GPT-4o for evaluation, we ensure that high scores stem from genuine reasoning capabilities, not from recognizing “familiar” token patterns.
- Human Validation: A random sample of 20% of the generated “Hard Negatives” was manually reviewed by human annotators to verify two conditions:
- Topical relevance: The text must discuss the correct entity/subject.
- Answer absence: The text must not contain the ground truth answer.
Tool versions & configuration
Benchmarks are snapshots in time. We evaluated the following library versions (as of Dec 2025):
Note: For all tools using LLMs, we enforced model=”gpt-4o” and temperature=0 to ensure deterministic comparison.
💡Conclusion
A key finding of this benchmark is the performance of “minimalist” prompting.
Others (Ragas/DeepEval): Rely on internal, complex “Chain-of-Thought” prompts that ask the model to break down sentences, analyze claims step-by-step, and output structured JSON.
Weights & Biases (Winner): Used a direct, zero-shot prompt without complex reasoning instructions.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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