I built a production-ready RAG backend (because most examples break in real life)
Most RAG (Retrieval-Augmented Generation) projects you see online are great demos. But try running them in production and you’ll quickly hit issues: no ingestion pipeline no async processing no scaling story no observability no proper deployment setup So I decided to build something that actually wo
ORIGINAL SOURCE →via Dev.to
ADVERTISEMENT
⚡ STAY AHEAD
Events like this, convergence-verified across 689 sources, land in your inbox every Sunday. Free.
GET THE SUNDAY BRIEFING →RELATED · tech
- [TECH] Anthropic launches 10 AI agents for finance
- [TECH] Samsung hits $1 trillion valuation as AI rally lifts shares over 10%
- [TECH] AMD sees 2nd-quarter revenue above estimates on AI spending
- [TECH] In the global AI race, a sanctioned Chinese firm says cheaper models can still win
- [TECH] OpenAI, Anthropic eye AI services deals
- [TECH] ElevenLabs adds high-profile investors after annualized revenue tops $500M