Ask HN: Should I Combine Global Knowledge, Internet Search, and User RAG
I'm building a SaaS platform in Sri Lanka that handles documents and other sensitive data.Each user can upload their own documents and information, and the platform uses RAG to answer questions based on that user's data. That part makes sense to me.My main concern is what happens when the user hasn't uploaded enough information. However, I'm not sure if I'm thinking about this correctly.I'd really appreciate hearing how others would approach this problem
- ▪I'm building a SaaS platform in Sri Lanka that handles documents and other sensitive data.Each user can upload their own documents and information, and the platform uses RAG to answer questions based on that user's data.
- ▪That part makes sense to me.My main concern is what happens when the user hasn't uploaded enough information.
- ▪However, I'm not sure if I'm thinking about this correctly.I'd really appreciate hearing how others would approach this problem
Hacker News (Ask HN) files mainly under programming. We currently carry 40 of its stories.
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 | Ycombinator |
| Canonical URL | https://news.ycombinator.com/item?id=49059969 |
| Publication time | Sun, 26 Jul 2026 16:55:11 +0000 |
| Retrieval time | 2026-07-26T19:54:40.684Z |
| Last seen | 2026-07-26T19:54:40.684Z |
| 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 | z5UzYk6n7M_r |
| 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
I'm building a SaaS platform in Sri Lanka that handles documents and other sensitive data.Each user can upload their own documents and information, and the platform uses RAG to answer questions based on that user's data. That part makes sense to me.My main concern is what happens when the user hasn't uploaded enough information. I still want the LLM to provide accurate answers using reliable information from the internet (or from a curated knowledge base), with proper citations.These are the two architectures I'm considering:Option 1:Base LLM (OpenAI/Anthropic via Azure AI Foundry or Amazon Bedrock) ↓ Platform RAG (global knowledge base managed by us) ↓ User-specific RAG In this approach, we maintain a global knowledge base that we (the platform admins) curate and update.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Ycombinator.