docs/docs5.txt

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.