As the deployment of Large Language Models (LLMs) like GPT expands across domains, mitigating their susceptibility to factual inaccuracies or hallucinations becomes crucial for ensuring reliable performance. This blog post introduces two novel frameworks that enhance retrieval-augmented generation (RAG): one uses summarization to achieve a maximum of 57.7% storage reduction, while the other preserves critical information through statement-level extraction. Leveraging DBSCAN clustering, vectorized fact storage, and LLM-driven fact-checking, the pipelines deliver higher overall performance across benchmarks such as PubMedQA, SQuAD, and HotpotQA. By optimizing efficiency and accuracy, these frameworks advance trustworthy AI for impactful real-world applications.