EXPLAIN03

Explain an AI research paper's core contribution in plain English

Turn a dense arXiv paper into a 5-minute read for a working developer.

The Prompt

Explain the AI research paper below to a working developer who knows ML basics but not the specific subfield.

Paper: {paper_title_or_abstract}

Deliver in this exact structure:
1. The one-sentence contribution (what's new here)
2. The problem it solves (with an example)
3. How the method works (3-5 bullets, no math beyond high-school)
4. What the results actually show (and what they don't)
5. When this matters for someone building products vs when it's academic

Try in Claude →Try in ChatGPT →

Example Output

Paper: "Chain-of-Verification Reduces Hallucination in Large Language Models"

1. Contribution: A prompting technique where the model generates verification questions about its own answer, then answers those, then produces a final response.
2. Problem: LLMs confidently invent facts. Existing fixes require retraining or external tools.
3. Method: Draft answer → generate 5-10 verification questions → answer each independently → synthesize final response using verified facts only.
4. Results: 20-40% hallucination reduction on factual QA benchmarks. Does NOT help on creative or opinion tasks.
5. Matters if: You're building a factual assistant, RAG app, or research tool. Academic if: you're doing creative writing, coding, or chat.

When to Use

Reading research above your subfield. Preparing a tech talk. Explaining a paper to your team without sounding hand-wavy.

Curated by Akash Rana, Editor