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#rag

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7 articles

Advanced RAG Architecture: Beyond Simple Vector Search

Advanced RAG: The Production Pipeline Simple Retrieval-Augmented Generation (RAG) is easy to build but hard to make accurate. To move beyond a basic prototype, you need an advanced architecture that optimizes every step…

Apr 20, 20262 min read

Deep Dive

#ai#llm#rag

DatabasesAdvanced

Vector Search in NoSQL: Redis and MongoDB as Vector Databases

Vector Search in NoSQL: The AI Evolution With the rise of Large Language Models (LLMs), Vector Search has become a critical requirement for Retrieval-Augmented Generation (RAG). While specialized databases like Pinecone…

Apr 20, 20262 min read

Deep Dive

#redis#mongodb#vector-search

AI/MLAdvanced

LLM Evaluation at Scale: LLM-as-Judge, RAGAS, and Building Automated Eval Pipelines

Evaluating LLMs is the unsolved problem at the center of every production AI system. You can't ship a prompt change, a retrieval improvement, or a model upgrade without knowing whether it actually got better — or just be…

Apr 8, 202611 min read

Deep Dive

#llm evaluation#ragas#llm-as-judge

AI/MLAdvanced

LLM Observability in Production: Traces, Evals, Cost, Latency, and Failure Modes

LLM systems fail differently from normal backend systems. A normal service usually fails with a timeout, an exception, or a bad status code. An LLM application can return a fluent answer that is wrong, unsupported by ret…

Apr 8, 202611 min read

Deep Dive

#llm#observability#evals

AI/MLAdvanced

Retrieval Systems for RAG: BM25, Dense Retrieval, Hybrid Search, and Reranking

Retrieval is the component that determines whether your RAG system produces accurate answers or confident hallucinations. The LLM is constrained by what you hand it. If the retrieved chunks are wrong, the generation is w…

Apr 8, 202616 min read

Deep DiveAI/ML Mastery

#retrieval#bm25#rag

AI/MLAdvanced

Vector Embeddings: The Foundation of Modern AI Applications

Every modern AI application — semantic search, RAG, recommendations, duplicate detection — is built on vector embeddings. An embedding converts text, images, or audio into a point in high-dimensional space where semantic…

Mar 11, 202511 min read

Deep Dive

#ai#embeddings#vector database

AI/MLAdvanced

Building a Production RAG System: Embeddings, Vector DBs, and Retrieval

Retrieval-Augmented Generation (RAG) is the most practical technique for making LLMs useful on your private data. Instead of hoping the model memorizes your documents during training (it doesn't), RAG retrieves relevant…

Feb 12, 202512 min read

Deep Dive

#ai#llm#rag