Beyond RAGs: Building Actually Truthful AI Harnesses
LLM ApplicationsBeyond RAGs: Building Actually Truthful AI HarnessesRetrieval is not evidence. How to build AI that proves its own claims.Ari Joury, PhDSeptember 24, 202611 min readThe future of AI is promising, but only if we're able to make it truthful. Image created with Leonardo AIPhotograph of layered ridgelines fading into warm dust haze, terracotta to pale peach gradient, soft flat light, infinite and quiet.
- ▪LLM ApplicationsBeyond RAGs: Building Actually Truthful AI HarnessesRetrieval is not evidence.
- ▪How to build AI that proves its own claims.Ari Joury, PhDSeptember 24, 202611 min readThe future of AI is promising, but only if we're able to make it truthful.
- ▪Image created with Leonardo AIPhotograph of layered ridgelines fading into warm dust haze, terracotta to pale peach gradient, soft flat light, infinite and quiet.
Towards Data Science files mainly under ai. We currently carry 179 of its stories.
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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 | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/beyond-rags-building-actually-truthful-ai-harnesses/ |
| Publication time | Thu, 24 Sep 2026 14:00:01 GMT |
| Retrieval time | 2026-09-24T14:05:26.716Z |
| Last seen | 2026-09-24T14:05:26.716Z |
| 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 | ltDOS48GnfJt · 1 stories |
| 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
LLM ApplicationsBeyond RAGs: Building Actually Truthful AI HarnessesRetrieval is not evidence. How to build AI that proves its own claims.Ari Joury, PhDSeptember 24, 202611 min readThe future of AI is promising, but only if we're able to make it truthful. Image created with Leonardo AIPhotograph of layered ridgelines fading into warm dust haze, terracotta to pale peach gradient, soft flat light, infinite and quiet. Image created with Leonardo AIThere's a category error that shows up in many LLM systems: We retrieve a few documents, put them in context, generate a fluent answer with links, and then we call the answer "grounded." That's often useful, but it's hardly enough in real life when you're trying to show real evidence for your claims and not just documents that make them more…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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