This is the first part of post series that serve as a concise guide to harnessing the possibilities of the Wolfram plugin for mathematics, physics and Wolfram Language coding, with a special focus on effective problem-solving prompting techniques, exemplified with 100+ plugin-using ChatGPT chat sessions.
Check out other parts of this guide here:
Guide 1: https://community.wolfram.com/groups/-/m/t/3070428​
Guide 2: https://community.wolfram.com/groups/-/m/t/3075538​
Guide 4: https://community.wolfram.com/groups/-/m/t/3077667​
Guide 5: https://community.wolfram.com/groups/-/m/t/3078038

Contents

The Importance of Prompt Engineering​ Prompt Examples (All from the Literature)

Cell coloring conventions
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The Importance of Prompt Engineering

Prompt Examples (All from the Literature)

Robot movements

Prompt a Robot to Walk with Large Language Models (https://arxiv.org/abs/2309.09969)

Table similarity
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Quiz making

PwR: Exploring the Role of Representations in Conversational Programming (https://arxiv.org/abs/2309.09495)

Improving course descriptions

Instruction Tuning with Human Curriculum (https://arxiv.org/abs/2310.09518)

Well-guided writing

Can Large Language Models Understand Real-World Complex Instructions? (https://arxiv.org/abs/2309.09150)

Dialog analysis

Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents (https://arxiv.org/abs/2310.09343)

Direct Unity game engine

LLMR: Real-time Prompting of Interactive Worlds using Large Language Models (https://arxiv.org/abs/2309.12276)

Assurance cases

Trusta: Reasoning about Assurance Cases with Formal Methods and Large Language Models (https://arxiv.org/abs/2309.12941)

Larger-scale coding

CodePlan: Repository-level Coding using LLMs and Planning (https://arxiv.org/abs/2309.12499)

Code quality improvements

Frustrated with Code Quality Issues? LLMs can Help! (https://arxiv.org/abs/2309.12938)

Robotics

GenSim: Generating Robotic Simulation Tasks via Large Language Models (https://arxiv.org/abs/2310.01361)

Kitchen activities

AgentTuning: Enabling Generalized Agent Abilities for LLMs (https://arxiv.org/abs/2310.12823)

Chemistry predictions

What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks (https://arxiv.org/abs/2305.18365)

Spatial trajectories

Large Language Models for Spatial Trajectory Patterns Mining (https://arxiv.org/abs/2310.04942)

Economics questions

Generative AI for Economic Research: Use Cases and Implications for Economists (https://www.newyorkfed.org/medialibrary/media/research/conference/2023/FinTech/400pm_Korinek _Paper _LLMs _final.pdf)

Game play reasoning

Strategic Reasoning with Language Models (https://arxiv.org/abs/2305.19165)

Authorship verification

Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification (https://arxiv.org/abs/2310.08123)

Student misconceptions

Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions (https://arxiv.org/abs/2310.02439)

Writing K-12 word problems

Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency (https://arxiv.org/abs/2310.06837)

Newton, Lagrange, Poincaré discuss the three-body problem

LLMs as Potential Brainstorming Partners for Math and Science Problems – Case Studies and Analysis (https://arxiv.org/abs/2310.10677)

Movie review reviewing

Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations (https://arxiv.org/abs/2310.11207)

Making causal graphs

Causal Inference Using LLM-Guided Discovery (https://arxiv.org/abs/2310.15117)

Solving stumpers

Decoding Stumpers: Large Language Models vs. Human Problem-Solvers (https://arxiv.org/abs/2310.16411)

Academic writing

Supercharging academic writing with generative AI: framework, techniques, and caveats (https://arxiv.org/abs/2310.17143)

Rhetorical role prediction in legal cases

Harnessing GPT-3.5-turbo for Rhetorical Role Prediction in Legal Cases (https://arxiv.org/abs/2310.17413)

Medical exam question generation

A ChatGPT Prompt for Writing Case-Based Multiple-Choice Questions (https://doaj.org/article/c26739e7c3b543f3b92f2341a583a3ee)

Identifying student misconceptions

Step-by-Step Remediation of Students’ Mathematical Mistakes (https://arxiv.org/abs/2310.10648)

Steganography

Preventing Language Models From Hiding Their Reasoning (https://arxiv.org/abs/2310.18512)

Real estate sales

Extracting user needs with Chat-GPT for dialogue recommendation (https://arxiv.org/abs/2310.19303)

Fine-grained constraints following

FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models (https://arxiv.org/abs/2310.20410)

CoT for vision

Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models (https://arxiv.org/abs/2309.04461)

“The Importance of Prompt Engineering” section continues in the following post.

CITE THIS NOTEBOOK

Guide 3: The Wolfram Plugin for ChatGPT​
by Michael Trott​
Wolfram Community, STAFF PICKS, December 4, 2023
​https://community.wolfram.com/groups/-/m/t/3077476