Can you describe a scenario where you had to adjust the weighting between components and how you validated the changes?

In a RAG system I was balancing the influence of retrieval versus generation. Initially, the system used a 70/30 split (70% retrieved context, 30% generated text). User feedback indicated hallucinations and low relevance. I introduced a tunable weight parameter that scales the retrieved context score before feeding it to the language model. To validate, I ran an A/B test with 10,000 queries, comparing the new 50/50 split against the baseline. Metrics included precision@k, recall@k, and MRR for retrieval, and BLEU and ROUGE for generation. Human evaluators also scored factual correctness on a 5‑point scale. The new weighting improved precision@1 by 12% and reduced hallucination incidents by 30%. I then performed a post‑deployment monitoring loop, adjusting the weight every month based on user satisfaction scores.