RAG Explained: How Retrieval-Augmented Generation Actually Works
Retrieval-Augmented Generation (RAG) is a method that enhances the efficiency of large language models (LLMs) by separating the retrieval and generation processes. It utilizes an ingestion pipeline to process documents and a query pipeline to respond to user requests with relevant information. By using vector databases and chunking techniques, RAG ensures that only the most pertinent data is retrieved, minimizing costs and improving response quality.
- ▪RAG consists of two main pipelines: an ingestion pipeline for processing documents and a query pipeline for handling user requests.
- ▪The method addresses challenges such as cost, context limits, and quality by extracting only the most relevant chunks of information.
- ▪Vector databases are preferred over traditional text searches because they capture meaning and allow for efficient similarity searches.
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Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/surajsharmaind/rag-explained-how-retrieval-augmented-generation-actually-works-dd7 |
| Publication time | Mon, 25 May 2026 11:56:02 +0000 |
| Retrieval time | 2026-05-25T12:07:36.708Z |
| Last seen | 2026-05-25T12:07:36.708Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | WxfnERcAIx55 · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1778532) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Suraj Sharma Posted on May 25 RAG Explained: How Retrieval-Augmented Generation Actually Works #ai #rag #llm #machinelearning The Two Phases of RAG RAG (Retrieval-Augmented Generation) splits into two separate pipelines: Ingestion pipeline — runs once (or on a schedule) to process your documents Query pipeline — runs live for every user request Why Not Just Send All Your Text to the LLM? Three hard problems: Cost — millions of tokens per query = $$$ Context limits — even 128K token…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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