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RAG for the AI SDK
A practical guide to implementing retrieval-augmented generation with the AI SDK — from ingesting documents into vectors to retrieving context at query time.
Gemini 2 Is the Top Model for Embeddings
Google released Gemini Embedding 2, their first natively multimodal embedding model. We ran it against 17 models across 7 datasets. It takes #1 with 1605 Elo, but the top three are within 18 points of each other.
GPT-5.4: OpenAI's RAG Regression
OpenAI released GPT-5.4 with a Pro variant for extended reasoning. We tested both on our LLM-for-RAG leaderboard across three workloads.
Zembed-1: The Current Best Embedding Model
ZeroEntropy released zembed-1, a 4B embedding model distilled from zerank-2 reranker. We tested it to see how it performs on real retrieval tasks.
Claude Sonnet 4.6 for RAG
We evaluated Claude Sonnet 4.6 in a RAG setup across factual retrieval, synthesis, and scientific tasks compared to frontier models.
Voyage 4: Evaluation Notes
We tested Gemini 3 inside an actual retrieval setup and compared it directly with GPT-5.1 across five areas that matter for RAG.
Claude Opus 4.6 Performance in RAG
We evaluated Claude Opus 4.6 in a RAG setup across factual retrieval, synthesis, and scientific tasks versus 11 frontier models.
How to Detect Hallucinations in RAG
RAG helps, but hallucinations still happen. We tested four detection methods to find the best for production—comparing accuracy, latency, and cost.
Multimodal vs Text Embeddings: Performance Comparison
We compared a text-based and a multimodal embedding pipeline across text, tables, and charts to see where multimodal actually helps.
Gemini 3 Flash: A strong factual RAG model
We evaluated Gemini 3 Flash in a RAG setup to understand where it excels and where it falls short—focusing on factual retrieval and grounding.
Cohere Rerank 4: A real upgrade over 3.5
We benchmarked Cohere Rerank 4 Pro and Fast against v3.5 and other rerankers under the same RAG pipeline.
GPT-5.2 RAG Performance: We Tested It
We plugged GPT-5.2 into our LLM RAG leaderboard and compared it against nine other frontier models under the same RAG pipeline.
Best Vector Databases for RAG
We reviewed seven popular vector databases to understand how they differ in deployment, cost, and where they fit in real RAG systems.
Opus 4.5 is the new best model for RAG
An evaluation of Opus 4.5 inside a real retrieval setup, compared against Gemini 3 Pro and GPT 5.1 across five behaviors that matter for RAG.
Gemini 3 vs GPT 5.1 for RAG
We tested Gemini 3 inside an actual retrieval setup and compared it directly with GPT-5.1 across five areas that matter for RAG.
Embedding models have converged
We compared 13 embedding models across 8 datasets using an LLM judge and ELO scoring. The result: almost all of them perform in the same narrow band.
Best Reranker for RAG: We tested the top models
We benchmarked eight leading rerankers to find which performs best for real-world RAG pipelines—comparing speed, accuracy, and relevance.
Cohere vs ZeRank: Which Reranker Actually Performs Better?
We compared Cohere v3.5 and ZeRank-1 in a RAG pipeline using a BEIR subset and a custom dataset — analyzing accuracy, latency, and LLM preference.
Building Effective RAG Pipelines: A Practical Guide
Learn how to design and implement robust retrieval-augmented generation (RAG) pipelines, from document processing to retrieval optimization.
Is RAG Dead?
OpenAI released the GPT 4.1 models supporting 1M token context window. Gemini supports up to 10M tokens in research. Is the RAG era over?
Automate Business Workflows with AI Agents
Discover how AI agents can transform business operations by automating complex workflows, reducing manual effort, and improving efficiency.
Building a Proof-of-Concept RAG System in an Afternoon
A practical guide to quickly building a functional retrieval-augmented generation system to demonstrate the value of AI-powered document search.
The Art of Document Chunking for LLM Applications
Explore the nuances of effective document chunking strategies for retrieval-augmented generation systems and how they impact LLM performance.
Parsing PDF Documents at Scale
Learn strategies and techniques to efficiently extract structured information from large volumes of PDF documents for use in AI applications.