GenAI Applications Evaluation Guidelines
GenAI teams are moving fast — but evaluation often remains ad hoc.
Unlike traditional software, GenAI applications are not always easy to test with simple input-output assertions. The same prompt can produce different valid responses, and quality often depends on hidden or intermediate steps: retrieved context, tool calls, memory state, agent handoffs, safety checks, and more.
That makes evaluation harder — but also more important.
This guide provides a structured way to think about GenAI evaluation across common application patterns, including RAG, tool use, memory, agents, multimodal systems, performance, and safety.
It is designed to help teams answer:
- What should we evaluate for this application?
- Which method should we use — manual review, code-based checks, LLM-as-judge, or a combination?
- How can we start implementing these evaluations in a repeatable way?
How to Use This Guide
Start with Strategy if you are defining an evaluation approach from scratch.
Then jump to the areas that match your application:
- Building a RAG app? Look at retrieval quality, context relevance, faithfulness, citations, and hallucination.
- Using tools or APIs? Evaluate tool selection, parameter correctness, execution success, and recovery from failures.
- Using memory? Check what gets stored, what gets retrieved, and whether memory introduces privacy, poisoning, or stale-context risks.
- Building agents? Evaluate not only the final answer, but also the trajectory: handoffs, intermediate decisions, role adherence, and unnecessary steps.
- Preparing for production? Use the Performance and Safety sections to evaluate latency, cost, reliability, guardrails, privacy, and harmful-output risks.
How This Guide Is Organized
| Section | What It Covers |
|---|---|
| Strategy | Evaluation methods, test data, ground truth, scoring, LLM-as-judge, human review, and code-based checks |
| Accuracy | Capability-specific evaluation for LLM responses, RAG, context provision, tool use, memory, multimodal systems, and agents |
| Performance | Latency, cost, throughput, reliability, and scalability under realistic usage conditions |
| Safety | Guardrails, privacy, bias and fairness, harmful content, prompt injection, and data leakage |
Each leaf node in the mind map links to a short practical guide. Some include code snippets or implementation examples that teams can adapt as a starting point.
The goal is not to prescribe one universal recipe. GenAI applications vary too much for that.
The goal is to help teams stop winging evaluation — and start making quality measurable, repeatable, and easier to discuss.
Start here: Strategy →