RAG (Retrieval-Augmented Generation) is the foundation of knowledge-grounded AI. But most RAG implementations fail because of poor pipeline design—not because of the AI model itself.
Don't use fixed-size chunks. For code, chunk by function. For articles, chunk by paragraph with headings preserved. For tables, chunk by row with structure intact.
Vector search understands meaning. Keyword search (BM25) understands exact terms. Combine them and you get the best of both worlds.
Use a lightweight cross-encoder model (like Cohere Rerank) to re-sort initial results. This consistently improves top-5 accuracy by 15-30%.
Tag your chunks with metadata (date, category, author) and filter before semantic search. This dramatically reduces noise.
export async function retrieveContext(query: string) {
const keywordResults = await searchIndex.keywordSearch(query);
const vectorResults = await vectorStore.similaritySearch(query);
const merged = [...keywordResults, ...vectorResults];
const ranked = await reranker.rerank(query, merged);
return ranked.slice(0, 5);
}
A well-optimized RAG pipeline is the difference between an AI that hallucinates and one that delivers expert-level accuracy.
Read the full deep-dive with chunking strategies, embedding model comparisons, and production deployment tips at JayApp.
Originally published at https://jayapp.cn/en/blog/nextjs-16-rag-pipeline-optimization