I Work in Healthcare Tech. Here's Why I Built a RAG Tool for Clinical Documents.
Monika Sonnad Math, a senior software developer in healthcare technology, created a Retrieval-Augmented Generation (RAG) tool to improve navigation through clinical documents. The tool aims to enhance accuracy in retrieving critical information, avoiding the pitfalls of AI models that may generate incorrect answers. By focusing on deterministic responses and grounding answers in actual document content, the tool addresses a significant inefficiency in healthcare workflows.
- ▪Monika Sonnad Math built a RAG tool to solve navigation issues in clinical documents.
- ▪The tool emphasizes accuracy by ensuring responses are based solely on document content.
- ▪RAG stands for Retrieval-Augmented Generation, which retrieves relevant document sections before generating answers.
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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/monikasonnadmath/i-work-in-healthcare-tech-heres-why-i-built-a-rag-tool-for-clinical-documents-3630 |
| Publication time | Wed, 03 Jun 2026 10:24:19 +0000 |
| Retrieval time | 2026-06-03T10:42:02.244Z |
| Last seen | 2026-06-03T10:42:02.244Z |
| 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 | 1azJ7STSbp_k |
| 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 === 3966298) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Monika Sonnad Math Posted on Jun 3 I Work in Healthcare Tech. Here's Why I Built a RAG Tool for Clinical Documents. #ai #programming #python #rag I didn't set out to build a RAG application. I set out to solve an annoying problem I kept watching happen. I work as a senior software developer in healthcare technology in Belfast. A big part of that job is understanding what actually slows them down, and figuring out where software can help.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).