RAG & Vector Search

Most "scalable system design" articles describe theoretical systems handling millions of users that...

A fintech RAG pipeline poisoned its own vector store and the LLM-as-a-judge validator approved every hallucination. The fix: gate writes with code.

RAG retrieval strategies without a vector database: BM25, keyword indices, knowledge-in-bundle — and when embeddings earn their cost.

We stopped measuring retrieval quality by how many tokens we could fit into the prompt. When...

A retrieval test asserted every relevance score sits in [0,1] — it failed on a live -1.83 silently corrupting a cite-or-refuse RAG safety gate.

The Problem When building BotForge, our AI no-code chatbot platform, we needed a retrieval...

Most RAG security talk is about prompt injection. Here's a risk almost nobody checks: the embedding...

In this blog post, we will see how to performance test a RAG (Retrieval-Augmented Generation)...

Several people posting lately about how RAG + search is not enough: "All the model gets is a list...

There is a point in the maturity arc of most enterprise RAG systems where the team has optimized the...

Stop Wasting Cloud Budget: Ingest & Chunk Unstructured Local Data in...

Introduction "Graph RAG typically pre-builds a global graph offline, then uses PageRank...

The same internal-docs Q&A problem every company has, solved on Azure AI Search and Azure OpenAI....

The same internal-docs Q&A problem every company has, solved on Vertex AI RAG Engine. Terraform...

Every company has the same problem - support agents and employees can't find answers buried in...

Improving retrieval quality beyond cosine top-k: hybrid dense+BM25 search with Reciprocal Rank Fusion, metadata filtering, chunking strategy, and measuring reca

Improving retrieval quality beyond cosine top-k: hybrid dense+BM25 search with Reciprocal Rank Fusion, metadata filtering, chunking strategy, and measuring reca

Building retrieval-augmented generation from first principles in Python: chunking, embeddings, a hand-rolled cosine-similarity vector search, reranking, and whe

🚀 My First Dev.to Post + My First RAG Project: InsightFetch Hey everyone! 👋 This is my...

Over the past few months, while building RAG applications, I kept noticing the same pattern. Every...

Standard RAG is great for static knowledge bases. Embed documents, embed a query, return top-k by...

Binary chunking trees boost information efficiency by roughly 6 percent while delivering relevance on...

The Question and Answer Chain node is n8n's built-in RAG (Retrieval-Augmented Generation) node. You...

Fail-closed groundedness, deterministic corroborators, and a self-test — because an evaluator should...

Build a working Retrieval-Augmented Generation pipeline from scratch in ~40 lines of Python — then see exactly where it breaks. Part 1 of a hands-on series.

A hands-on walkthrough of a retrieval-augmented QA pipeline over PubMed abstracts, the evaluation...

Hearing "RAG" everywhere in AI but not 100% sure how it works? 🤔 I just put together a quick...

Want to build an AI assistant that talks to your company documents? First you need to answer one...

Learn how to build semantic observability for production RAG systems to catch hallucinations and irrelevant responses before users notice.

Build a corrective RAG pipeline with LangGraph that grades retrieval quality, rewrites bad queries, and generates cited answers.

It is a noticeable thing when a microservice fails. It is either a null pointer exception in a Java...

If you run a RAG system in production, you eventually hit a dilemma that has nothing to do with your...

Building Droste: a local structural + semantic code-memory engine for MCP agents AI coding agents...

Retrieval-Augmented Generation demos beautifully. Embed your documents, run a similarity search, drop...

Vector similarity search is powerful but it has a well-known weakness: exact term matching. If a user...

For the last year, everyone has been talking about one architecture. RAG. Retrieval-Augmented...

In the previous article, we built AI Agents that autonomously search our pgvector database. One...

In the previous article, we covered the three core concepts behind RAG. Now let's build it. By the...

RAG pipelines fail in quiet ways. Retrieval drops. Documents go missing. Metadata gets corrupted...

If your RAG gives wrong answers, the model is rarely the problem-retrieval handed it the wrong context. Why retrieval is the real 2026 bottleneck.

RAG is one of those patterns that sounds more complicated than it has to be. At its core,...

Most enterprise RAG failures are not model failures. They are infrastructure failures. The demo...

Provisioning a Bedrock RAG knowledge base with S3 Vectors, without the hallucinated API calls. If...

DSPy Reliability, RAG/Agentic AI Patterns, & Parallel Agent Orchestration ...

Every RAG tutorial pulls the same move. It walks you through embeddings, chunking, retrieval, and...

I almost shipped a RAG pipeline that, on certain questions, cited exactly the right document — and...

In v2 I added hybrid retrieval (FAISS + BM25) to fix keyword blindspots. All 19 test questions...

A user types a question into your RAG system. Before anything is retrieved, something decides what...