RAG
33 items tagged with "rag"
Best Practices4
Retrieval-Augmented Generation (RAG) Best Practices
RAG grounds a large language model in external documents retrieved at query time, reducing hallucination and letting answers reflect current, private data without retraining the model.
Vector Database Best Practices
A vector database stores embeddings and serves fast similarity search for AI features like RAG and semantic search; best practices cover indexing, metadata, and freshness.
Fine-Tuning vs RAG Decision Framework
A decision framework for choosing between fine-tuning, RAG, or both, based on whether the goal is new knowledge, consistent behavior, freshness, or domain adaptation.
Hallucination Mitigation
Hallucination mitigation reduces confident but false LLM output through grounding, retrieval, citation, verification, and uncertainty handling so answers can be trusted.
Models3
Qwen3.6-Plus
A long-context Qwen model variant listed on OpenRouter, intended for general-purpose instruction following and long-document workloads.
Mistral Small 2603
A new Mistral Small series release listed on OpenRouter with a 262k context window, positioned as a general-purpose foundation model for long-context workloads.
Qwen3.5-9B
A 9B-parameter Qwen3.5 foundation model with a large (262k token) context window, positioned for general chat and reasoning with long-context inputs.
Reference Architectures4
Vector Search Platform for RAG
A vector search platform on Azure powering retrieval-augmented generation with embeddings, a vector database, and hybrid search.
Retrieval-Augmented Generation Platform on AWS
A reference design for a production RAG system on AWS that grounds LLM answers in private documents using a managed vector store and Bedrock models.
Vector Database Platform on Kubernetes
A reference design for a self-hosted, multi-tenant vector database platform on Kubernetes that powers semantic search and RAG retrieval at scale.
Conversational AI Assistant on Azure
A reference design for an enterprise conversational assistant on Azure that combines RAG, tool calling, and memory behind a governed orchestration layer.
Playbooks2
LLM and RAG Application Rollout Playbook
A program for rolling out a retrieval-augmented generation application from prototype to governed production with evals, guardrails, and observability.
Vector Database Adoption Playbook
A program to adopt a vector database for semantic search and RAG, covering embeddings, indexing, scaling, and operations.
Stacks5
RAG Stack (LangChain + pgvector + LLM)
Retrieval-augmented generation stack: LangChain orchestrates retrieval over pgvector embeddings in Postgres and grounds an LLM's answers in your own data.
LangGraph + pgvector
An agentic LLM application stack using LangGraph for stateful agent workflows backed by Postgres with pgvector for retrieval and memory.
Haystack RAG
A production RAG stack built on the Haystack framework for composable retrieval-augmented generation pipelines over a document store.
LlamaIndex + Qdrant
A RAG and data-framework stack pairing LlamaIndex for LLM data ingestion and querying with Qdrant as a high-performance vector database.
Amazon Bedrock RAG
A managed RAG stack on AWS using Amazon Bedrock foundation models with Knowledge Bases for retrieval over data stored in S3.
Comparisons7
pgvector vs Pinecone
A Postgres extension that adds vector search to your existing database versus a fully managed, purpose-built vector database.
RAG vs Fine-Tuning
Retrieval-augmented generation injects external knowledge at query time; fine-tuning bakes behavior into model weights. They solve different problems and often combine.
LangChain vs LlamaIndex
LangChain is a broad framework for LLM application orchestration; LlamaIndex specializes in data ingestion and retrieval for RAG. They overlap but optimize for different centers of gravity.
Pinecone vs Weaviate
Pinecone is a fully managed vector database; Weaviate is open source with managed and self-hosted options. The split shapes control, operations, and feature flexibility.
Weaviate vs Qdrant
Weaviate and Qdrant are open-source vector databases. Weaviate emphasizes a modular, feature-rich platform; Qdrant emphasizes a lean, performant Rust-based engine.
pgvector vs Dedicated Vector Database
pgvector adds vector search to PostgreSQL; a dedicated vector database is purpose-built. The choice trades operational simplicity against scale and specialized features.
Embeddings vs Keyword Search
Embedding-based semantic search matches by meaning; keyword search matches by terms. Each handles different query types, and hybrid search often beats either alone.
Benchmarks3
MTEB (Massive Text Embedding Benchmark)
A broad benchmark for text embedding models spanning classification, clustering, retrieval, reranking, and semantic similarity across many datasets and languages.
BEIR (Benchmarking Information Retrieval)
A heterogeneous zero-shot retrieval benchmark that tests how well a single retrieval model generalizes across diverse domains and query types without task-specific training.
FRAMES (Factuality, Retrieval, And reasoning MEasurement Set)
A benchmark for retrieval-augmented generation that tests end-to-end factuality, multi-document retrieval, and multi-hop reasoning on questions needing several sources.
FAQs2
What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation is a technique that supplements a language model with relevant documents fetched at query time, rather than relying onl...
What is the difference between fine-tuning and RAG?
Fine-tuning updates a model's weights by training it further on domain-specific examples, changing how the model behaves and what style or skills it h...