This aggregates CodeNodeX AI community articles and discussions related to RAG, covering concept explanations, tool comparisons, engineering practices, common issues, and indie hacking experience.
If you're researching RAG, start with high-quality long-form articles, then join post discussions for a complete understanding-to-practice path.
Putting documents in a vector database and using top-k retrieval is only RAG's "hello world." This article covers four levers for improving RAG quality: chunking, hybrid retrieval, reranking, and query rewriting, plus a reproducible evaluation method that takes RAG from "barely usable" to genuinely accurate.
Covers tech trends, engineering practices, and indie developer perspectives around large models, RAG, and AI.
Technical trends, engineering practices, and an independent developer's perspective on RAG and large language models.
An independent developer's perspective on technical trends and engineering practices involving RAG and AI agents.
Technical trends, engineering practices, and an independent developer perspective on RAG and large language models.
Covers tech trends, engineering practices, and indie developer perspectives around large models and RAG.
As memory grows, you can't save tokens by overwriting old summaries. This article designs fixed input snapshots, derived lineage, key-fact fidelity tests, invalidation propagation, and reversible switching.
For RAG and semantic search, choosing a vector database is unavoidable. This article compares pgvector, Qdrant, Milvus, Chroma, and Pinecone by data scale, latency, operational cost, and hybrid search, with clear recommendations for when to use each.
"Tuning RAG without an eval set is just guesswork." This guide uses RAGAS to explain four core metrics, building and running an evaluation set, and diagnosing issues so "it feels better" becomes "recall rose from 0.72 to 0.91."
`resources/updated` only signals that a URI may have changed; it carries no content version. This article uses discovery generations, subscription leases, and read-digest reconciliation to handle lost notifications, out-of-order updates, and resource-set changes during pagination.
Using pgvector but finding search slow? This guide explains pgvector performance tuning: choosing IVFFlat vs. HNSW, tuning parameters (lists/m/ef_search), hybrid search (vector + full text), filtering metadata before search, and best practices for million-scale datasets, so pgvector runs quickly and accurately.
Telling a model not to disclose information during generation is not access control. This article explains identity context, ACL filtering, cache isolation, citation validation, and unauthorized-access testing so the model never sees documents the user should not access.