Navigating the Prompt Space: Improving LLM Classification of Social Science Texts Through Prompt Engineering
Navigating the Prompt Space: Improving LLM Classification of Social Science Texts Through Prompt Engineering explores This paper explores prompt engineering techniques to improve LLM classification accuracy for social science texts, finding that minimal context increases yield the best results.. Commercial viability score: 4/10 in LLM Prompt Engineering.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisPrompt Programming for Cultural Bias and Alignment of Large Language ModelsViability: 3.0
- Builds On ThisBeyond the Answer: Decoding the Behavior of LLMs as Scientific ReasonersViability: 3.0
- Builds On ThisWithin-Model vs Between-Prompt Variability in Large Language Models for Creative TasksViability: 2.0
- Builds On ThisInfluencing LLM Multi-Agent Dialogue via Policy-Parameterized PromptsViability: 3.0
- Builds On ThisNatural Language Declarative Prompting (NLD-P): A Modular Governance Method for Prompt Design Under Model Drift