RAG & Vector Search

How embedding models, chunking, retrieval modes, and query phrasing jointly determine Code-RAG quality.

Extends an earlier model-selection benchmark to three model families (Japanese / Western / Chinese)...

The first few times a RAG system gave me a bad answer, I did what I think everyone does: I went and...

Vector RAG is the reflexive answer to "give the model more context," and when I built a production...

I Cut RAG Costs 65% With DeepSeek + ChromaDB — Full Data Last quarter my team burned through $14,800...

Most RAG pipelines embed the user's raw query and skip the cheapest recall win there is: rewriting it before search.

When people say they are "adding RAG" to a workflow, the conversation often jumps too quickly to...

Manual test runs aren't enough. Wire your RAG test framework into GitHub Actions so quality checks run automatically on every knowledge base update, system prom

Stop writing one-off tests. Learn how to combine retrieval quality, faithfulness, and edge case testing into a single structured, reusable RAG test framework yo

I keep seeing teams ship a "RAG system" that's really a vector database with a thin wrapper. They...

Imagine your team just deployed a sleek RAG-based docs assistant for the SaaS platform you develop....

Happy path testing isn't enough. Learn the edge cases that silently break RAG systems in production — empty knowledge bases, conflicting context, out-of-scope q

Good retrieval doesn't guarantee a good answer. Learn what faithfulness means in RAG, why LLMs hallucinate even with perfect context, and how to detect it autom

There is a phase in every enterprise RAG deployment that I think of as the ingestion...

How I built a RAG agent to eliminate operational interruptions at work Open source...

RAG Explained: How Retrieval-Augmented Generation Actually Works What Is...

I'm an automation tester. Usually my job is simple: the same input should give the same output, every...

Most "chat with your documents" demos work in an afternoon. Then you hit the last 20%: retrieval that...

At 2 a.m., our ops group chat exploded — “The support bot is mixing up historical orders from the...

Most RAG tutorials stop at something like: Vector Search → LLM → Done And for learning the basics,...

Introduction: The Place of Large Models in RAG and Lingering Questions Retrieval-Augmented...

My wife runs an agency that helps tech founders grow their LinkedIn presence. That means dozens of...

In This Article Why RAG Over Fine-Tuning for Financial Documents Chunking Strategy for Financial...

In the era of generative AI, simply asking a model a question is no longer enough. The real value...

Remember the early days of web security? We thought a simple firewall was enough. Then came SQL...

In This Article Why RAG Over Fine-Tuning for Financial Documents Chunking Strategy for Financial...

Most RAG tutorials I found were either "pip install langchain and you're done" or 50-page academic...

A RAG (retrieval-augmented generation) assistant is supposed to answer from your documents, not from...

How I built Claimify ESG — scoring 2,200+ climate claims from sustainability reports against NGO evidence, with company-scoped retrieval, two-step classificatio

Chunk clean article content for embeddings, summarization, and full-text search—skip nav, clap bars,...

RAG SOTA: I Built SEQUOIA and Tested 7 Pipelines — Full Results After 20+ hours of compute...

Your RAG demo works on localhost. Under real load—socket exhaustion, 429s, and pool timeouts kill it. A frontend dev's walkthrough of naive vs production Node.j

A few weeks ago, I had a classic "works on my machine" moment. I had built a nice RAG prototype...

"The output filter runs after the LLM has already seen the confidential data. By then, three classes...

After 20+ hours of compute time on local hardware, I benchmarked 7 RAG configurations against real-world tasks. SEQUOIA (RAPTOR tree + step-back prompting) cons

RAG SOTA: I Tested 7 Pipelines and Built SEQUOIA (Open Source) After 20+ hours of compute...

Most RAG frameworks today assume: a huge dependency graph mandatory LLM orchestration opinionated...

Every RAG pipeline I've reviewed this year hits the same decision point: which vector store do you...

The bug nobody talks about in JS RAG tutorials You wire up a Node.js retrieval pipeline....

Most RAG blog posts read like product brochures. After building a few systems over the last months...

Key Takeaways Storing vectors in an Oracle VECTOR column alongside content, metadata, and...

Sparse means thinly spread, scattered, or not dense. In sparse embeddings, chunks are converted into...

You ask ChatGPT about your company's internal policies. It makes something up. It sounds confident....

A composable drift detector for RAG and agent systems. Five orthogonal dimensions, one DriftReport, bring your own embedder and time-series store.

A 1536-dimensional float32 embedding is 6 KB. A corpus of 10 million documents is roughly 60 GB of...

Backpressure For Streaming Pipelines Last time we showed how to terminate non-cooperative...

I shipped a personal RAG bot spike in 25 minutes. SQLite FTS5 with unicode61 returned 0 hits for every Chinese query. Bigram preprocessing made it work without

RAG has two places to fail: retrieval and generation. Most teams only catch one. Here's the complete...