The science is theirs. The workflow is ours.

Google, OpenAI, and Anthropic document what works. We condensed it into a workflow you can use today.

The Problem: Too Many Sources, No Clear Path

Prompt engineering research is everywhere: Google's 7-component framework, Anthropic's 9 techniques, OpenAI's 6 strategies, plus 1,500+ academic papers.

For an analyst or consultant, that's not helpful—it's overwhelming. You need a workflow you can use today, not a reading list for next month.

Google Gemini Docs

7 technical components (public documentation)

Anthropic Claude Docs

9 sequential techniques (public documentation)

OpenAI Docs

6 general principles (public documentation)

What Research Shows

OpenAI o1-preview achieves 78.3% accuracy on medical diagnostics

Harvard Medical School & Stanford University Study, 2024

Few-shot examples improve accuracy significantly

Brown et al., "Language Models are Few-Shot Learners", 2020

Chain-of-thought prompting: 17.9% → 58.1% accuracy on math problems

Wei et al., "Chain-of-Thought Prompting", Google Research, 2022

Format structure matters more than word count

Schulhoff et al., "The Prompt Report" (1,500+ papers analyzed), 2025

Our Synthesis: 5 Building Blocks

We studied official documentation from Google, OpenAI, and Anthropic, plus 1,500+ academic papers. Then we condensed it into a workflow you can use today.

1

Task

What you want the AI to do. Action verbs, clear scope, defined boundaries.

2

Context

Who the output is for, why it matters, what constraints apply.

3

Output

Format, length, tone, and what to avoid. Structure beats guesswork.

4

Examples

1-3 samples showing what good looks like. Teaches implicitly, significantly improves accuracy.

5

Persona

The role the AI should take. Analytical (default), Professor, Consultant, Editor, or Researcher.

What Makes Us Different

The science is theirs. The workflow is ours.

AI-Powered Quality Scoring

AI analyzes your prompt structure and provides specific feedback. Red/Yellow/Green scoring shows exactly what's missing. Fix it instantly.

Structured Framework

Five building blocks. One powerful framework. Task → Context → Output → Examples → Persona. Follow our proven structure and get reliable results.

Your Prompt Wallet

Build once. Organize forever. Folders, version history, and instant search. Your prompt library grows with you.

Analytics-Focused

Built specifically for data analysis, reporting, and decision-making. For analysts, consultants, and data teams who need better outputs.

BYOK (Bring Your Own Key)

Your API keys. Your costs. Your control. Works with any provider. No markup on API usage. No vendor lock-in.

The Evidence

Our methodology is based on peer-reviewed research and official documentation.

Task + Context + Output Framework

Google Gemini: "Instructions + Context + Format" (Prompt Design Strategies, 2025)

Anthropic Claude: "Clear instructions + Context" (Prompt Engineering Overview, 2024-2025)

We condensed these into Task → Context → Output for clarity.

Examples (Few-Shot Learning)

Research: Brown et al., "Language Models are Few-Shot Learners" (OpenAI, 2020)

Finding: Few-shot examples significantly improve accuracy, with major gains after just 1-2 examples. Common practice uses 2-5 examples for balance between performance and context.

Our approach: Recommend 1-3 examples for practical use.

Persona (Role Prompting)

Anthropic Claude: "Give Claude a role" (Technique #6)

Google Gemini: Mentions "persona" in context section

Our approach: Made it a dedicated block with clear options (Analytical, Professor, Consultant, Editor, Researcher).

Methodology: Based on publicly available documentation from Google, OpenAI, and Anthropic, plus peer-reviewed academic research. All sources are cited.

Engineer prompts that perform. Today.

No hype. No guesswork. Just research-backed prompt engineering.

Start Building Today

Disclaimer: Google, Gemini, OpenAI, ChatGPT, Anthropic, and Claude are trademarks of their respective owners. This page references publicly available documentation for educational and comparative purposes. We are not affiliated with, endorsed by, or sponsored by these companies.