RAG is evolving from blunt recall to smarter, policy-aware retrieval. Hierarchical Agentic RAG trims drift by using tiny, precise child chunks for retrieval anchors and larger parent passages for context [1]. The idea—claimer: “Agentic RAG”—is to let agents help fetch what’s needed, grounding search plans in data.
Hierarchical and Agentic RAG Small chunks boost precision; big chunks keep the full story. This two-tier setup is pitched as a balance of speed, cost, and answer quality [1].
Production-ready RAG architectures • Retriever-Reranker (The Precision Stack) – Top-K recall with a fast hybrid search; then CrossEncoder(q, d) re-scores. Pros: precision wins. Cons: more latency and cost. Implementations include Turbopuffer and ZeroEntropy [2].
• Query Transformer (The Recall Stack) – The LLM refines the query (Multi-Query/HyDE) before search, improving recall but adding an upfront cost [2].
• Graph RAG (The Connections Stack) – Uses a graph query language for multi-hop questions. Great for structure but requires upfront data modeling and can be rigid [2].
Multilingual retrieval challenges Bilingual setups fail when queries and docs are in different languages. Mixed languages and multilingual embeddings don’t always map cleanly; practitioners suggest indexing in a single language or translation strategies, with examples like Alibaba's multilingual embedding model in play [3].
Reranker improvements with synthetic data Synthetic data plus LLM supervision boost transformer-based rerankers, addressing nuance and negation better [4].
Policy-driven Local RAG With Llama 3:8b, policy files in JSON guide what prompts are allowed. Embedding choices matter, and deployment often runs on HPC clusters [5].
Closing thought: expect more policy-aware, multilingual RAG engines that ferry safety and accuracy into production.
Referenced POST IDs: [1], [2], [3], [4], [5]
References
Hierarchical Agentic RAG: What are your thoughts?
Explores hierarchical RAG (child vs parent chunks), agentic RAG, retrieval-precision versus context richness, with experiences and repo reference.
View sourceI built 50+ RAGs in 2 years. Here are the architectures that get products out the door!
Discusses three RAG patterns (Retriever-Reranker, Query Transformer, Graph RAG); CrossEncoder surpasses naive BiEncoder for precision.
View sourceMultilingual RAG chatbot challenges – how are you handling bilingual retrieval?
Discusses bilingual RAG retrieval challenges, prompts, multilingual embeddings; asks for effective strategies and practical approaches from practitioners with examples globally.
View sourceEnhancing Transformer-Based Rerankers with Synthetic Data and LLM Supervision
Discussion on improving transformer-based document rerankers with synthetic data and LLM supervision for better ranking performance in search systems today.
View sourceImplementing Local Llama 3:8b RAG With Policy Files
Experimenting with Llama 3:8b for RAG and policy-driven blocking; seeks embedding options and metrics guidance on HPC.
View source