LLMs as Planners, Not Reasoners

What it does

A hybrid question-answering system that combines LLMs , Wikidata , and Prolog to perform structured reasoning over real-world knowledge . User Question ↓ LLM Classifier → (Prolog vs direct LLM) ↓ Entity + Search Plan Extraction ↓ Wikidata Search + Expansion ↓ Convert to Prolog Facts ↓ LLM generates Prolog Query ↓ Execute in SWI-Prolog ↓ LLM formats final answer 🧠 Key Ideas Not RAG → builds a structured KB on the fly Symbolic reasoning → Prolog executes actual logic LLM as planner, not source of truth Fallback mechanism → if logic fails, revert to LLM 📂 Project Structure . ├── gemini_prolog.py # Main pipeline ├── answer.py # Answer generation from Prolog results ├── entity_extraction.py #…from github.com

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• HP
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  Hi HN. I heard you like dev tools and AI, so we wanted to share our project that we’ve been working on. We’re working on Horizon [1] - a higher level abstraction for LLMs so that developers can spend less time trying to grapple with LLMs to make them work and more time with users. This is the starting feature set which takes an auto-ML approach to identify the optimal LLM model, hyperparameters, and prompt - instead of just giving you the tooling to figure it out yourself. You can read more about it in our documentations. Our view is that as LLMs become increasingly commoditized and prompts…

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• AG
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