Master DeepEval with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make DeepEval powerful for testing & quality workflows.
Comprehensive metric suite covering hallucination detection, answer relevancy, faithfulness, contextual precision/recall, tool correctness, conversational coherence, knowledge retention, bias, toxicity, and more — each validated against human judgment benchmarks.
Running a full quality audit on a customer support chatbot using hallucination, relevancy, and faithfulness metrics to catch responses that fabricate information or drift from the knowledge base.
Tool correctness metric specifically evaluates whether AI agents call the right tools with correct parameters and in the right sequence — essential for validating agent behavior in production.
Testing an e-commerce agent to verify it correctly calls the inventory API before the order API, passes valid product IDs, and handles out-of-stock scenarios without hallucinating availability.
Write LLM tests using familiar pytest patterns with decorators and assertions. Tests run alongside unit tests in existing CI/CD pipelines. Failed quality thresholds block deployments automatically.
Adding DeepEval tests to a GitHub Actions pipeline that runs on every pull request — if hallucination scores exceed 10% or relevancy drops below 0.85, the PR can't merge.
Generate diverse test datasets from your documents using LLMs. Creates edge cases, adversarial inputs, and comprehensive test coverage without manual data curation.
Generating 500 test questions from a product documentation corpus, including paraphrases, multi-hop questions, and out-of-scope queries to stress-test a RAG chatbot.
Automated adversarial testing that generates prompt injection attempts, bias probes, toxicity triggers, and jailbreak prompts to test agent robustness before deployment.
Running red-team evaluations against a customer-facing agent to verify it resists prompt injection, doesn't generate biased responses, and handles toxic inputs gracefully.
Cloud platform layering on DeepEval with LLM tracing (full context: inputs, outputs, tool calls, latency, token costs), real-time monitoring, performance alerting, collaborative dataset management, prompt versioning, and dashboards. Available as SaaS or self-hosted.
Monitoring a production RAG system's quality in real-time — receiving alerts when hallucination rates spike, drilling into individual traces to identify root causes, and tracking quality trends across model versions.
DeepEval is broader — it covers RAG metrics (contextual precision, recall, faithfulness) plus agent tool use evaluation, conversational quality metrics, bias/toxicity detection, and red-teaming. RAGAS focuses specifically on RAG pipeline evaluation with deeper RAG-specific metrics. If you only need RAG evaluation, RAGAS may be sufficient. For comprehensive agent and LLM testing, DeepEval covers more ground.
Yes. DeepEval includes conversational metrics for coherence, topic adherence, and knowledge retention across multiple conversation turns. The chat simulation feature in Confident AI Premium can generate multi-turn test conversations automatically.
Yes. DeepEval evaluates inputs and outputs regardless of framework. It works with LangChain, CrewAI, LlamaIndex, OpenAI Agents SDK, custom agents, and any LLM application that produces text outputs.
DeepEval metrics are validated against human judgment benchmarks. Accuracy varies by metric and evaluator model — using stronger models (GPT-4, Claude) as evaluators produces more accurate scores. The framework's 50+ metrics are research-backed and regularly updated based on academic findings.
DeepEval is the free, open-source evaluation framework for running LLM tests locally or in CI. Confident AI is the commercial cloud platform built by the same team — it adds collaboration, dataset management, LLM tracing, real-time monitoring, alerting, and dashboards. DeepEval works standalone; Confident AI layers on top for team and production use.
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Tutorial updated March 2026