RAG with MongoDB
Retrieval-augmented generation needs somewhere to store and search the embeddings it retrieves context from — and increasingly, teams are choosing to keep that vector data in the same database already holding their application's operational data, rather than standing up a...
Improving RAG Retrieval
Introduction: The Retrieval Bottleneck The generation phase of RAG receives most of the attention. Developers obsess over prompt engineering, model selection, and temperature settings. But generation quality is fundamentally bounded by retrieval quality. If the retrieved...
Building a Multi-Document RAG System
Introduction: From Single File to Knowledge Base A single-document RAG system answers questions about one PDF or text file. A multi-document RAG system answers questions across an entire corpus: hundreds of product manuals, thousands of research papers, or millions of support...
Building a PDF RAG System
Introduction: The Most Common RAG Use Case PDF documents are the lingua franca of enterprise knowledge. Contracts, research papers, technical manuals, financial reports, and legal briefs all arrive in PDF format. Building a RAG system that can ingest PDFs, answer questions...
Semantic Search Explained
Introduction: Beyond Keyword Matching For decades, information retrieval was dominated by keyword search. Users typed words, and systems returned documents containing those words. This approach works well for known-item searches, where the user knows exactly what terms to...
Exact Search vs Approximate Search
Every vector search system faces the same fundamental choice: guarantee the mathematically correct answer, or accept a very good answer in exchange for much greater speed. This is the exact-versus-approximate search trade-off, and picking the right side of it — often for...
What is a Vector Database?
Introduction: Databases for the AI Era Traditional databases are designed for exact matching and range queries. They store rows of structured data and retrieve them based on precise conditions. A SQL query like SELECT * FROM products WHERE price < 100 is fast and...
Text Embeddings Explained
Introduction: Turning Language into Mathematics Text embeddings are the invisible engine behind modern information retrieval. They are the reason a search for "automobile" can find documents about "cars," why a question in English can retrieve answers in Spanish, and why a...
Chunk Overlap Explained
Introduction: The Boundary Problem When a document is split into chunks for embedding and retrieval, a subtle but serious problem emerges at the boundaries. A sentence at the end of one chunk might depend on a sentence at the beginning of the next. A definition in chunk one might be …
Choosing the Right Chunk Size
Introduction: The Chunking Dilemma In a Retrieval-Augmented Generation system, documents must be divided into pieces before they can be embedded and stored. This division, called chunking, is one of the most consequential design decisions in the entire pipeline. Choose chunks...
What is RAG?
Introduction: The Knowledge Gap in Large Language Models Large Language Models are trained on vast corpora of internet text, books, and code. They can write poetry, debug software, and explain quantum mechanics. But they have a critical limitation: their knowledge is frozen...