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...
Vector Search Performance Optimization
A vector search system that performs beautifully in a demo with a thousand test vectors can slow to a crawl once it's handling millions of embeddings and real production traffic. Getting vector search to perform well at scale isn't one fix — it's a set of levers spanning...
Reducing RAG Hallucinations
Introduction: When RAG Lies Despite the Evidence Retrieval-Augmented Generation was supposed to solve hallucinations. By grounding the model in retrieved documents, we assumed it would stick to the facts. But RAG systems hallucinate too. They ignore retrieved context and...
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...
ANN Search Explained
When you search a database of a million vector embeddings for the ones closest to your query, checking every single one is often too slow to be practical at scale. Approximate Nearest Neighbor (ANN) search is the technique that makes vector search fast enough for real...
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...
RAG vs Fine-Tuning
Introduction: Two Paths to Customization When organizations want an LLM to perform well on their specific domain, they face a strategic choice between two fundamentally different approaches. Retrieval-Augmented Generation augments the model's context at inference time by...
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...
Building a LangChain Chatbot
Introduction: Beyond Question Answering A basic chatbot that calls an LLM and returns the response is trivial to build. A production-ready chatbot that maintains context, retrieves relevant documents, handles streaming, and manages conversation state is a different challenge...
LangChain Vector Stores
Introduction: Databases for Meaning Traditional databases excel at exact matching. They can find a user by email or filter orders by date with precision and speed. But they fail at semantic matching. If you search for "automobile," a traditional database will not return...
LangChain Embeddings
Introduction: The Bridge Between Language and Mathematics Embeddings are the invisible foundation of modern retrieval systems. At their core, embeddings are dense numerical vectors that capture the semantic meaning of text, images, or other data types. When you convert a...
Context Engineering vs Prompt Engineering
As AI applications have grown more sophisticated — pulling in documents, memory, tool outputs, and conversation history rather than just a single well-crafted instruction — a new term has entered the vocabulary: context engineering. It's often used alongside, or even instead...