Fine-Tuning Embedding Models for Your Domain
Fine-tuning an embedding model for your domain means teaching a general-purpose model which pieces of text should be…
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Structured articles covering everything from core concepts to production operations.
Comparing Embedding Models with MTEB
MTEB, short for Massive Text Embedding Benchmark, helps compare embedding models across many tasks such as retrieval, classification,…
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How to Choose an Embedding Model
Choosing an embedding model is a retrieval decision, not just a machine learning decision. The best model for…
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How to Choose Chunk Size and Overlap
Chunk size and overlap determine what an AI database stores, retrieves, and sends into a generation model. Small…
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Chunking Strategies for RAG
Chunking is one of the most important design choices in retrieval-augmented generation because it decides what text gets…
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Building Your First RAG Pipeline
A first RAG pipeline is a system that loads your own data, breaks it into searchable chunks, turns…
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What Is Retrieval-Augmented Generation (RAG)?
Retrieval-augmented generation, or RAG, is a way to make a generative AI system answer with information retrieved from…
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Vector Databases in the LLM Stack
Vector databases help large language model applications find the right information before the model generates an answer. They…
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How to Choose the Right Index for Your Workload
The right index for an AI database workload depends on what you are optimizing for: exactness, latency, memory…
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Score Fusion: RRF vs Relative Score Fusion
Score fusion is the process of combining results from different retrieval methods, such as keyword search and vector…
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