Testing
Stripe provides a test mode with real API behavior, test card numbers for different scenarios, and a CLI for webhook testing. Testing a payment integration requires covering happy-path charges, declined cards, 3D Secure flows, webhook receipt and idempotency, refunds, and subscription lifecycle events. This guide covers all of them. Payment
Testing
Basic MCP server tests cover tool invocation and resource reading. Advanced patterns go further: property-based testing to discover unexpected inputs, fuzz testing for protocol robustness, multi-client concurrency testing, transport-layer validation, and performance benchmarking. This guide covers those advanced patterns. A previous guide covered the basics of testing MCP servers — unit
Testing
Syrupy is the most popular snapshot testing plugin for pytest. It stores snapshots in __snapshots__/ directories alongside your test files, uses YAML-like .ambr format for human-readable diffs, and integrates with pytest's assertion rewriting for clear failure messages. This guide covers installation, writing snapshot tests, updating snapshots, and handling
Testing
AI agent pipelines need CI/CD just like any software — but standard pipelines don't account for LLM non-determinism, evaluation costs, or model-level regressions. This guide covers building CI/CD pipelines specifically for AI agent systems: fast deterministic checks, LLM evaluation gates, model upgrade workflows, safety guardrails, and production
Testing
RAG (Retrieval-Augmented Generation) systems fail in ways that don't look like failures. The answer is fluent, confident, and wrong. The retrieved context was relevant but the LLM ignored it. The retrieved context was irrelevant and the LLM hallucinated around it. Without systematic testing, these failures reach users invisibly.
Testing
RAG evaluation frameworks exist because manual inspection doesn't scale. You can't read 10,000 (question, answer, context) triples and judge whether the model is hallucinating, whether the retrieved context is relevant, or whether the answer actually addresses the question. RAGAS and TruLens automate this judgment using
Testing
Vector databases are infrastructure. Like any database, they can be misconfigured, queried incorrectly, or updated in ways that corrupt your index. Unlike relational databases, the failure modes are subtle: incorrect metadata filters silently narrow results, wrong namespace routing returns data from a different index, stale vectors from deleted documents still
Testing
Embeddings are the foundation of semantic search, RAG systems, and recommendation engines. When embedding behavior is wrong — whether because the model changed, the preprocessing changed, or the similarity calculation is buggy — everything downstream breaks silently. Search returns irrelevant results. RAG systems hallucinate. Recommendations get weird. Testing embeddings requires thinking about
Testing
Chunking is one of the most impactful and least-tested parts of a RAG pipeline. How you split documents into chunks directly determines retrieval quality: too small and chunks lose context; too large and they include irrelevant content that confuses the LLM; wrong boundaries and questions about sentence-end information miss the
Testing
LangChain and LlamaIndex are composable systems. A RAG chain is a sequence of components: a retriever, a prompt template, an LLM, an output parser. Each component can fail, and their integration can fail in ways that none of the individual components would catch in isolation. Integration testing for RAG chains
Testing
PDF testing requires a layered approach: text content assertions, page structure checks, visual regression diffs, and accessibility validation. This guide covers strategies that work regardless of how your PDFs are generated — Puppeteer, WeasyPrint, iText, PDFBox, ReportLab, or any other tool — and shows how to combine them into a CI-ready pipeline.
Testing
Multi-agent systems (LangGraph workflows, CrewAI crews) are dramatically harder to test than single agents. State flows between agents, handoffs can fail silently, and emergent behaviors arise from agent interactions. This guide covers testing strategies for multi-agent orchestration: state validation, handoff testing, agent isolation, and failure injection. Single agents are hard