Hybrid AI: Combining Deterministic Analytics with LLM Reasoning
The article discusses the challenges faced in developing an agentic AI system for manufacturing operations. It highlights the issues of generating plausible but incorrect analytics due to the limitations of current AI models. A proposed solution involves separating deterministic data analysis from LLM-based reasoning to improve reliability.
- ▪The initial prototype of the AI system produced mostly incorrect results despite appearing promising.
- ▪AI models like ChatGPT and Microsoft Copilot exhibited patterns of generating convincing but fabricated outputs.
- ▪The author emphasizes the need for deterministic execution in foundational data analysis to ensure reliable results.
Towards Data Science files mainly under ai. We currently carry 104 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/hybrid-ai-combining-deterministic-analytics-with-llm-reasoning/ |
| Publication time | Fri, 22 May 2026 16:30:00 +0000 |
| Retrieval time | 2026-05-22T16:37:02.520Z |
| Last seen | 2026-05-22T16:37:02.520Z |
| 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 | -kbAHtz6nzPn |
| 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
Agentic AI Hybrid AI: Combining Deterministic Analytics with LLM Reasoning How AI architecture prevents plausible but wrong analytics Ingo Nowitzky May 22, 2026 19 min read Share Generated by author using ChatGPT Introduction I tried to build an agentic AI network for my company that advises manufacturing plants on how to mature their operations. The system was designed to be data-driven, allowing users to upload assessment data directly through the chat interface. The first working prototype was finished surprisingly quickly, and at first glance the results looked promising. There was only one problem: Most of the results were wrong! Even worse, the AI quickly learned which numerical ranges looked plausible and began generating convincing — but fabricated — outputs.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
Discussion
0 commentsMore from Towards Data Science
