Building With RAG: A Practical System
A second-pass model that reorders retrieved chunks by true relevance to the query. In RAG, vector search returns a candidate set that's fast but noisy. This guide pulls together everything on Onexial tagged rag — 7 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind RAG
Before wiring anything together, the vocabulary has to be precise. These 2 definitions cover the terms that show up in almost every RAG discussion — each one links to a full entry with an example and its own connections inside the graph.
The RAG tool stack
A stack is a set of tools chosen for one job, not a list of favourites. These 3 stacks show which combinations hold up in production for RAG, and what each layer is actually responsible for.
AI Support Agent Stack
Tier-1 support handled by an AI agent grounded on your docs, with human handoff.
RAG Context Enrichment Stack
A technical stack designed to enrich the contextual data provided to a Retrieval Augmented Generation (RAG) system, improving the quality and depth of LLM responses.
Low-Cost RAG Stack
This stack combines open-source and cost-efficient components to build a Retrieval-Augmented Generation (RAG) system with minimized operational expenses.
Trade-offs and comparisons
Most RAG decisions are trade-offs rather than right answers. These 1 comparisons break down the real differences, when each option wins, and the recommendation for the common case.
Prompts you can reuse
Prompts are reusable components. Each of these 1 prompts is written to be dropped into a RAG workflow with minimal editing, including the context it expects and an example output.
Frequently asked questions
- What is Reranking?
- In RAG, vector search returns a candidate set that's fast but noisy. A reranker (usually a cross-encoder like Cohere Rerank or bge-reranker) scores each candidate against the query and reorders them, dramatically improving answer quality.
- What is an example of Reranking?
- Retrieve top 50 chunks by vector similarity → rerank down to top 5 → send only those to the LLM. Latency +200ms, accuracy often +20–40%.
- Why does Reranking matter for AI and automation?
- A second-pass model that reorders retrieved chunks by true relevance to the query. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is Chunking?
- Chunking decides what unit of text your retriever returns. Too small → loses context. Too big → wastes tokens and dilutes relevance. Common strategies: fixed-size, sentence, semantic, and structural (by heading/section).
- What is an example of Chunking?
- A 40-page PDF split into ~800-token overlapping chunks by markdown heading typically outperforms naive 500-char splits on Q&A accuracy.
- Why does Chunking matter for AI and automation?
- Splitting documents into retrievable pieces before embedding them for RAG. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is the difference in RAG vs Long Context Windows?
- Massive context windows changed the calculus but didn't kill RAG. Cost, latency, freshness and precision all still favor retrieval for most production workloads.
- What are the main points of comparison?
- Cost per query: RAG: cheap, only relevant chunks vs Long context: expensive, full doc every time · Latency: RAG: fast (~1s retrieval + gen) vs Long context: slow (multi-second prefill) · Freshness: Update the index, done vs Re-send everything each request · Precision: Better with good chunking + reranking vs 'Lost in the middle' still real · Setup complexity: Higher (embedding, storage, retrieval) vs Just paste and prompt