Oracle says its new Trusted Answer Search can deliver reliable results at scale in the enterprise by scouring a governed set of approved documents using vector search instead of large language models (LLMs) and retrieval-augmented generation (RAG).
Available for download or accessible through APIs, it works by having enterprises define a curated “search space” of approved reports, documents, or application endpoints paired with metadata, and then using vector-based similarity to match a user’s natural language query to the most relevant of pre-approved target, said Tirthankar Lahiri, SVP of mission-critical data and AI engines at Oracle.
Instead of retrieving raw text and generating a response, as is typical in RAG systems that rely on LLMs, Trusted Answer Search’s underlying system deterministically maps the query to a specific “match document,” extracts any required parameters, and returns a structured, verifiable outcome such as a report, URL, or action, Lahiri said.
A feedback loop
Three weeks into testing, a learner told me my AI tutor gave her the wrong answer.
Not obviously wrong — just outdated enough to mislead.
That was the moment I realized something most RAG systems quietly ignore: they have no sense of time. My system retrieved the most similar document, not the most current one. And in a knowledge base that changes constantly, that’s a serious flaw.
The fix wasn’t in the retriever or the model. It was in the gap between them.
I built a temporal layer that filters expired facts, boosts time-sensitive signals, and makes the system prefer what’s still true — not just what matches.
The post RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production appeared first on Towards Data Science.
Oracle’s abrupt termination of an estimated 20,000 to 30,000 employees via email on March 31 has sparked significant employee pushback over what many regarded as inadequate severance. The company offered four weeks of base pay plus one additional week per year of service, capped at 26 weeks, but crucially did not accelerate unvested stock grants — meaning […]
RAG is a model that connects large language models to live agency knowledge bases — enabling grounded, mission-specific responses, rather than generic outputs.
Building a RAG system just got much easier. Google’s File Search tool for the Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, indexing are all managed for you. And with the latest update, it’s gone multimodal. You can now search through both text and images in a single […]
The post Gemini API File Search: The Easy Way to Build RAG appeared first on Analytics Vidhya.
Modern frontend applications rely on cloud services for far more than basic data fetching. Authentication, search, file uploads, feature flags, notifications and analytics often depend on APIs and managed services running behind the scenes. Because of that, frontend reliability is closely tied to cloud reliability, even when the frontend team does not directly own the infrastructure.
This is often one of the biggest mindsets shifts for frontend engineers. We often think about failure as a total outage where the whole site is down. In practice, that is not what most users experience. More often, the interface is partially degraded: A dashboard loads but one panel is empty, a form saves but the confirmation never arrives, or a file upload stalls while the rest of the page still appears normal.
That is why I think frontend resilience deserves more attention in day-to-day engineering conversations. The goal is not to prevent every cloud issue. That is rarely realistic. The more practical g
