Define AI agent tests before spending time and money
Ideas around RAG, memory, agents, graphs, entity extraction, chunking, reranking, and embedding should be tested with clear measures before heavy building starts. Asking AI to generate code, renting a , and running scattered experiments can burn days without showing whether the idea actually works. A failed result may come from a weak idea, a bad , or a flawed test, and those causes need to be separated.
Retrieval can be measured with Precision@k, Recall@k, MRR, nDCG, and Context Entity Recall. Answer generation can be checked with , Rate, , and Answer Relevancy. The full system can also be judged by citation accuracy, sub-question coverage, compound accuracy, latency, , TPS, and cost per query.
s such as NQ, TriviaQA, SQuAD, PopQA, HotpotQA, 2WikiMultiHopQA, MuSiQue, Bamboogle, and CRAG can be used for more comparable tests.
Key points
- Set evaluation measures before building a complex agent or RAG system.
- Separate failures caused by the idea, the , and the test method.
- Measure , answer quality, end-to-end behavior, and system cost separately.
- Cost per query, latency, , and TPS matter for token and budget control.
- s make experiments easier to compare.