Featured
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 performs best and where its limitations show, focusing on factual retrieval, reasoning depth, 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 under identical conditions to find which one performs best for real-world RAG pipelines — comparing speed, accuracy, and LLM-judged 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.