Retrieval-Augmented Generation

Software development has entered a new era where Muktesh Mishra argues that evaluation-driven development must replace traditional testing. In non-deterministic systems like LLMs, the same input can yield wildly different results, making standard unit tests insufficient. Developers must now architect Evals that account for subjectivity and qualitative performance rather than just binary success or failure. Data is the bedrock of evaluation One data set is never enough. To build a robust AI application, you must curate multiple data sets tailored to specific user flows. Start small with **synthetic data** to validate basic outputs, but quickly transition to a continuous refinement process. This involves labeling data according to application prospects and observing system behavior in real-time to ensure the data remains relevant as the model evolves. Moving beyond generic similarity scores A universal metric for AI does not exist. A Retrieval-Augmented Generation (RAG) system requires measuring retrieval accuracy and usefulness, whereas code generation demands a focus on functional correctness and robustness. When evaluating agents, the focus shifts to **trajectory evaluation**. We must analyze the specific paths an agent takes to execute a task, rather than just the final output. This includes multi-turn simulations to test how agents handle complex, conversational state changes. Balancing speed and fidelity at scale Scaling Evals requires a strategic trade-off between automated speed and human fidelity. While automation provides rapid feedback, certain subjective nuances still require a human in the loop. The most effective strategy involves caching intermediate results to prevent redundant computation and running evaluations frequently through parallel orchestration. Success lies in a cycle of measuring, monitoring, and iterating until the system aligns with business goals.