Guides
Practical guides for shipping better AI
Short, technical guides on testing, production behavior, remediation, guardrails, and AI reliability.
Topics
What the guides cover
Test before launch
- AI agent testing
- LLM testing
- Synthetic scenarios
- Multi-turn testing
- Tool failure testing
Learn from production
- AI observability
- LLM observability
- Agent tracing
- Failure clustering
- User intent and knowledge gaps
Improve what failed
- AI remediation
- Root-cause analysis
- AI incident management
- Regression scenarios
- Validation
Protect the live path
- PII handling
- Prompt injection
- Monitor versus Enforce
Build for a specific app
- AI agents
- Conversational AI
- RAG applications
- LangChain
- LangGraph
- OpenTelemetry
Coming first
The first twelve guides
- How to Test an AI Agent Beyond the Happy PathComing soon
- LLM Observability: What to Capture and Why It MattersComing soon
- Agent Tracing: From User Goal to Final OutcomeComing soon
- Synthetic Scenarios for Testing Real AI BehaviorComing soon
- AI Agent Evaluation Across Tools, State, and OutcomesComing soon
- How to Debug an AI Agent That Took the Wrong ActionComing soon
- RAG Evaluation: Retrieval, Context, and GenerationComing soon
- Chatbot Testing for Multi-Turn ConversationsComing soon
- LLM Guardrails: Monitor First, Then EnforceComing soon
- AI Remediation: From Failure to Validated ImprovementComing soon
- LangChain Observability for Real User OutcomesComing soon
- LangGraph Observability Across Nodes, Tools, and StateComing soon
Start with one AI app
The guides help. Connecting a real application helps faster.