Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter
  Impact on Performance and Efficiency
Authors: name: Adel Ammar, name: Anis Koubaa, name: Omer Nacar, name: Wadii Boulila

Large language models achieve high task performance yet often hallucinate or
rely on outdated knowledge. Retrieval-augmented generation (RAG) addresses
these gaps by coupling generation with external search. We analyse how
hyperparameters influence speed and quality in RAG systems, covering Chroma and
Faiss vector stores, chunking policies, cross-encoder re-ranking, and
temperature, and we evaluate six metrics: faithfulness, answer correctness,
answer relevancy, context precision, context recall, and answer similarity.
Chroma processes queries 13% faster, whereas Faiss yields higher retrieval
precision, revealing a clear speed-accuracy trade-off. Naive fixed-length
chunking with small windows and minimal overlap outperforms semantic
segmentation while remaining the quickest option. Re-ranking provides modest
gains in retrieval quality yet increases runtime by roughly a factor of 5, so
its usefulness depends on latency constraints. These results help practitioners
balance computational cost and accuracy when tuning RAG systems for
transparent, up-to-date responses. Finally, we re-evaluate the top
configurations with a corrective RAG workflow and show that their advantages
persist when the model can iteratively request additional evidence. We obtain a
near-perfect context precision (99%), which demonstrates that RAG systems can
achieve extremely high retrieval accuracy with the right combination of
hyperparameters, with significant implications for applications where retrieval
quality directly impacts downstream task performance, such as clinical decision
support in healthcare.