More complex prompts tend to elicit superior results with advanced LLM versions (authors' observation)¶
Claim · All claims
Evidence · 1 study · designr1·q1
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q1 i? The authors report that, as expected, more complex prompts tend to elicit superior results with advanced versions of LLMs, within the LEAP interface. → Steinert 2024
Evidence¶
Steinert 2024¶
Steinert, S., Avila, K. E., Ruzika, S., Kuhn, J., & Küchemann, S. (2024). Harnessing large language models to develop research-based learning assistants for formative feedback. Smart Learning Environments. https://doi.org/10.1186/s40561-024-00354-1
q1 · i? · design · r1
The authors' own observation while customizing and testing prompts within the LEAP interface for the Newton's cradle example. No systematic evaluation, criteria, or statistics are reported for this result.
"As expected, we found that more complex prompts tend to elicit superior results with advanced versions of LLMs."
Discussion¶
Related Claims¶
- Higher-quality prompt engineering skills predict the quality of LLM output in goal-directed use of generative AI tools — related
- Cognitive leap is the most prevalent deviation pattern of LLM-generated questions, and fine-grained prompting amplifies upward shifts while suppressing cognitive regression — related
- Within prompt–response AI interfaces, students without guidance rarely move beyond prompt tuning, yielding interactions of limited educational value and increased reliance on outputs — related
- Systematic prompting of an LLM can generate cognitively activating formative feedback aligned with research-based scaffold types (demonstrated, not yet empirically validated) — related
- Teacher-provided correct answers and pre-prompting reduce erroneous LLM output and hallucinations compared with open chatbot use — related
- The authors observe that more specific physical descriptions in generation prompts tend to produce more physically coherent AI-generated motion — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
complex-prompts-elicit-better-llm-results |
title |
More complex prompts tend to elicit superior results with advanced LLM versions (authors' observation) |
description |
More complex prompts tend to elicit superior results with advanced LLM versions (authors' observation) |
status |
review |
evidence_strength |
weak |
newest_evidence |
2024 |
generated |
by process:wiki-ingest on 2026-10-16 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — steinert-2024 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.