The Myth
"Just add RAG" has become the default answer to LLM hallucinations. Ground the model in your own documents, the thinking goes, and it will stop making things up. Retrieval-augmented generation does help, but it doesn't do what most teams assume it does. The gap between the two shows up exactly when the stakes are highest: legal citations, financial figures, medical claims, anything a user might act on.
RAG retrieves text that is similar to the query and hands it to the model as context. It says nothing about whether that text is true, current, or even relevant to the specific claim the model is about to make.
What RAG Actually Solves
To be clear about the real win first: RAG is genuinely effective at extending an LLM's knowledge beyond its training cutoff and grounding answers in a specific corpus instead of the model's parametric memory. That alone reduces a large class of hallucinations: the ones caused by the model simply not having the information.
Why Retrieval Alone Still Hallucinates
The failure modes that remain are structural, not a matter of tuning chunk size or embedding model. Three show up repeatedly in production RAG systems:
- ●Similarity is not relevance. Vector search returns chunks that are topically close to the query, not chunks that are legally or factually correct for the claim being made. In a head-to-head evaluation on legal research tasks, baseline RAG systems frequently returned topically similar but legally irrelevant documents when no applicable source existed, risking confident-sounding misinformation instead of an honest 'no answer.'
- ●No temporal grounding. A retrieved chunk carries no notion of when it was true or whether it still is. A document that was accurate a year ago and a document that is accurate today can look identical to a similarity search, so outdated numbers get surfaced with the same confidence as current ones.
- ●No verdict. RAG returns text, not a judgment. The model still has to decide, on its own, whether the retrieved passage actually supports the claim it's about to generate, and that's the same reasoning step that produces hallucinations in the first place.
What a Verification Layer Adds
This is the layer RAG doesn't have, and it's what the Factagora API is built around: a Temporal Knowledge Graph (TKG) that stores facts as time-stamped, causally linked FactBlocks instead of loose document chunks. Two properties follow directly from that design.
- ●Point-in-time answers. Facts are bi-temporal: each one records when an event occurred and the window during which it stayed valid. Queries return only what was true at the moment being asked about, so later information can't leak backward into an earlier answer.
- ●A verdict, not just a passage. The Fact Checker endpoint takes a claim and returns a TRUE / FALSE / UNCERTAIN verdict backed by evidence, instead of leaving the model to infer truthfulness from retrieved text. The Evidence Finder endpoint goes a step further and returns credibility-scored sources tagged by stance (supports, contradicts, or neutral), so a claim isn't just 'grounded,' it's actively checked against sources that disagree with it, too.
How to Fix This in Your Stack
The fix isn't replacing your RAG pipeline. It's adding a verification step after generation, not just retrieval before it:
- ●Keep your existing vector DB and RAG pipeline for context retrieval and drafting. That part works.
- ●After the model drafts an answer, extract the factual claims it makes.
- ●Run each claim through a Fact Checker call to get a TRUE / FALSE / UNCERTAIN verdict with supporting evidence.
- ●Use Evidence Finder to attach credibility-scored, stance-labeled citations to the claims that pass, and flag or suppress the ones that don't.
The result is an answer that's still generated by your RAG stack, but nothing reaches the user without being checked against a source that can say when it was true and whether it's still contested. The fastest way to try this is to get a free API key and call Fact Checker on a claim your own RAG pipeline already generated.