All topic guides

RAG & Vector Search

From your first retrieval pipeline to graph RAG and semantic caching.

10 articles · free · read in any order, best in this one

Retrieval-Augmented Generation is how you get an LLM to answer from your data instead of its training set — and it's also where most real-world LLM projects quietly fail. This guide is a curated path through every RAG and vector-search article on this site, ordered so each piece builds on the last: what RAG actually does, how embeddings and hybrid search decide retrieval quality, and the production patterns (and failure modes) that separate demos from systems.

Start here

What RAG is, and the context-window problem it exists to solve.

01

How Retrieval-Augmented Generation (RAG) Works

RAG grounds LLM responses in retrieved documents rather than model weights. Walk through the full pipeline, indexing, retrieval, augmentation, and generation, and where each sta...

02

Context Window Limits: Why Your LLM Still Hallucinates

A 128K context window doesn't mean the model attends equally across all of it. Token budget pressure, retrieval gaps, and the lost-in-the-middle problem explain most LLM halluci...

Embeddings & search quality

Retrieval quality is decided long before the LLM sees a token.

03

How to Generate Better Embeddings for Vector Search

Bad embeddings kill retrieval before the LLM even sees the query. Preprocessing strategies, model selection, chunking decisions, and fine-tuning approaches that move the needle ...

04

Embedding Models: Training, Fine-Tuning, and Optimization for Retrieval

Embedding quality determines what your retrieval system can find. How contrastive training works, when to fine-tune versus use off-the-shelf models, and what MTEB benchmark scor...

05

Hybrid Search: Combining Keyword and Vector Search for Better Retrieval

Pure vector search misses exact matches. BM25 misses semantic intent. Reciprocal rank fusion combines both without the tuning overhead of learned fusion, and outperforms either ...

06

Vector DB Comparison: Pinecone vs Weaviate vs Qdrant

Pinecone is fully managed. Weaviate adds hybrid search and schema flexibility. Qdrant is self-hosted with Rust-level throughput. Which one fits your RAG architecture depends on ...

Advanced patterns

Beyond plain top-k retrieval.

07

Graph RAG: When Knowledge Graphs Beat Vector Search

Standard vector RAG fails on multi-hop questions that require connecting information across multiple documents. Graph RAG solves this by building a knowledge graph of entities a...

08

Building Real-Time Chatbot Memory with Vector Databases + LLMs

Stateless LLMs forget everything between turns. Combining short-term context buffers with long-term vector memory gives chatbots the persistence that real-world use cases actual...

09

Semantic Caching for LLMs: Cut Your API Bill by 60%

Exact-match caching fails for LLMs because users never ask the same question twice in identical words. Semantic caching solves this by comparing query embeddings instead. A cach...

Production reality

Why most RAG systems fail — and what the survivors do differently.

10

Why Most RAG Systems Fail in Production

Poor chunking, weak embedding models, and retrieval that returns irrelevant context are why RAG fails, not the generator. A production-focused breakdown of the failure modes and...

Tools & resources

Free utilities that pair with this guide.

Token Counter See how much of your context window retrieval is eating. LLM Cost Calculator Price the API side of your RAG pipeline.

Keep going

Guides that pick up where this one ends.

AI Agents LLMs in Production