Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From
AI engineers today talk a lot about agentic AI, and the principle is simple: let the model decide. For a general-purpose assistant, that is fine: let the agent try, watch what it does. For an enterprise RAG process, it is dangerous.
- ▪AI engineers today talk a lot about agentic AI, and the principle is simple: let the model decide.
- ▪For a general-purpose assistant, that is fine: let the agent try, watch what it does.
- ▪For an enterprise RAG process, it is dangerous.
Towards Data Science files mainly under ai. We currently carry 140 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
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/before-full-agentic-rag-know-how-you-decide-and-the-parsing-methods-you-pick-from/ |
| Publication time | Wed, 12 Aug 2026 16:30:00 +0000 |
| Retrieval time | 2026-08-12T16:36:31.691Z |
| Last seen | 2026-08-12T16:36:31.691Z |
| 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 | 2Ge7rBZewL_t · 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
Large Language Model Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From Enterprise Document Intelligence [Vol.1 #5nonies] – Nature, plan, execute, synthesize: closing brick 1 with a dispatcher that reads each PDF’s nature and picks the method that fits, fitz, Docling, PaddleOCR, EasyOCR, MinerU or Surya, then folds the outputs into one corpus angela shi Aug 12, 2026 15 min read Share Photo by Caleb Oquendo, via Pexels. AI engineers today talk a lot about agentic AI, and the principle is simple: let the model decide. For a general-purpose assistant, that is fine: let the agent try, watch what it does. For an enterprise RAG process, it is dangerous. The answers feed real decisions, so we have to know every step and control the flow between the steps.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
Discussion
0 commentsMore from Towards Data Science
