Advanced Retrieval Strategies for RAG Apps
Explores pre‑ and post‑retrieval techniques—including chunking, indexing, query expansion, reranking, and hybrid search—for improving RAG pipeline effectiveness.
A step‑by‑step overview of advanced RAG methods, covering:
1 . Pre‑retrieval – Chunking strategies (fixed, semantic, dynamic) to manage context https://www.sagacify.com/news/a-guide-to-chunking-strategies-for-retrieval-augmented-generation-rag?utm_source=chatgpt.com
- Cleaning, embedding, and indexing docs https://superlinked.com/vectorhub/articles/advanced-retrieval-augmented-generation?utm_source=chatgpt.com
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Retrieval – Dense, sparse, and hybrid vector search approaches https://medium.com/%40social_65128/the-ultimate-guide-to-understanding-advanced-retrieval-augmented-generation-methodologies-467cd05a2ecd – Query expansion/transformations to enhance relevance https://www.comet.com/site/blog/advanced-rag-algorithms-optimize-retrieval/?utm_source=chatgpt.com
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Post‑retrieval – Filtering and reranking via cross‑encoders or LLM-based rerankers https://www.comet.com/site/blog/advanced-rag-algorithms-optimize-retrieval/?utm_source=chatgpt.com – Code examples using LlamaIndex and Hugging Face in colab https://superlinked.com/vectorhub/articles/advanced-retrieval-augmented-generation?utm_source=chatgpt.com
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Evaluation – Assessing retrieval accuracy, latency, and hallucination risk https://arxiv.org/abs/2501.07391?utm_source=chatgpt.com
Why this resource? This is one of the most practical and comprehensive tutorials on advanced RAG methods, combining industry best practices (chunking, hybrid search, reranking) with actionable examples and references to tools like LlamaIndex and vector stores.
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