| RAG Evaluation – Best Practices for Retrieval-Augmented Generation Systems | |
| Evaluation is a critical step when building a Retrieval-Augmented Generation (RAG) system. A successful RAG system must not only retrieve relevant | |
| documents but also generate accurate, grounded responses based on that context. Without proper evaluation, errors in retrieval or generation can slip | |
| into production, causing misleading answers or user frustration. | |
| First, treat the retrieval and generation components separately. For retrieval, measure how well the system finds useful documents: metrics like | |
| precision@k (how many of the top k retrieved are actually relevant) and recall@k (how many relevant documents were retrieved) help you locate | |
| weaknesses in your vector store or embedding model. For generation, assess whether the answer is correct, relevant, coherent and faithful to the | |
| retrieved context. If your agent produces fluent text but it’s not grounded in the retrieved material, you’ll face trust issues. | |
| Second, build a structured test set early. Select a variety of realistic questions that reflect how users will use the system. For each, define | |
| expected outcomes or “gold” answers when possible. By using the same test set across iterations, you can compare performance when you change chunking | |
| methods, vector stores, or prompts. This consistency ensures that improvements are measurable and meaningful. | |
| Third, automate the evaluation process. Setup scripts or pipelines that run the test set, compute metrics, record results, and plot trends. This | |
| way you can track regression, monitor when performance drops (for example if the knowledge base changes), and set thresholds for when to alert for | |
| human review. Continuous monitoring is especially important as your document base grows or becomes dynamic. | |
| Finally, remember that evaluation is ongoing—once you deploy your agent, user behaviour will evolve, documents will change, and queries will shift. | |
| Plan periodic re-evaluation (e.g., monthly or after major updates), refresh test sets, and maintain logs of system decisions. By doing so, you ensure | |
| your RAG assistant stays reliable and effective over time. |