Testing
AI agent workflows are harder to test than traditional software because the intermediate steps (which tools get called, in what order, with what arguments) are non-deterministic. This guide covers deterministic testing strategies: behavioral assertions over tool calls, golden dataset testing, state-based verification, and contract testing for agent-tool
Testing
MCP (Model Context Protocol) servers expose tools, resources, and prompts to AI agents. Testing them requires validating JSON-RPC protocol compliance, schema correctness, tool execution behavior, resource content, and error handling — none of which standard unit test frameworks handle out of the box. This guide covers how to test each
Testing
Off-the-shelf eval frameworks (DeepEval, Ragas, TruLens) cover 80% of use cases. For the remaining 20% — specialized domains, proprietary metrics, internal tooling constraints — you need to build your own. This guide walks you through designing metrics, curating datasets, implementing an LLM judge, and wiring it all into CI. When
Testing
TruLens is an open-source observability and evaluation framework for LLM applications. Its core contribution is the RAG Triad — three metrics (groundedness, context relevance, answer relevance) that together diagnose where a RAG pipeline breaks. Traces are stored locally or remotely, and a built-in dashboard makes debugging fast. What TruLens
Testing
LangSmith gives you production observability for LLM applications — full request traces, cost tracking, latency breakdowns, and human annotation queues. Combined with its evaluation layer, you can compare prompt versions, run automated evaluators, and catch regressions before they reach users. Why Tracing Matters for LLM Applications An LLM application isn'
Testing
Promptfoo is the standard tool for prompt regression testing and LLM red-teaming. Define test cases in YAML, run them against multiple models simultaneously, and catch prompt regressions before they reach production. The red-team mode automatically probes for jailbreaks, prompt injection, and safety failures. The Problem Promptfoo Solves Every
Testing
Ragas gives you a rigorous metric suite for RAG pipelines: faithfulness, answer relevancy, context precision, and context recall. Each metric isolates a different failure mode — bad retrieval vs. bad generation vs. incomplete recall. This guide shows you how to compute them, interpret scores, and integrate Ragas into CI. The RAG
Testing
DeepEval brings unit testing discipline to LLM applications. Write assertions on model outputs the same way you write pytest assertions on function return values — with G-Eval metrics, faithfulness checks, and hallucination detection. This tutorial walks you from installation to CI-integrated test suite. Why LLM Applications Need Unit Tests
AI Testing
RAG applications fail at multiple points — poor chunking, irrelevant retrieval, unfaithful generation. Testing each layer independently gives you clear signal on where failures originate. This guide covers practical testing strategies for every stage of the RAG pipeline. Key Takeaways RAG has three distinct failure modes. Retrieval failure (wrong chunks returned)
AI Testing
AI agents are harder to test than static LLMs because they make sequential decisions, call tools, and can compound errors across many steps. Testing them requires a combination of unit tests for individual tools, trajectory evaluation for decision sequences, and end-to-end tests for goal completion. This guide covers
AI Testing
DeepEval, PromptFoo, and LangSmith each solve a different version of the LLM evaluation problem. DeepEval provides deep metric-based evaluation for RAG and conversational AI. PromptFoo excels at prompt comparison and red-teaming. LangSmith integrates tightly with LangChain for tracing and production monitoring. This guide breaks down when to use
AI Testing
Prompts are code — they should be versioned, tested, and reviewed like any other critical system component. This guide covers the practices that teams use to test prompts systematically before shipping changes and catch regressions as models evolve. Key Takeaways Treat prompts as first-class artifacts. Store them in version control,