RRepoGEO

REPOGEO REPORT · LITE

keirp/automatic_prompt_engineer

Default branch main · commit eac521c7 · scanned 6/21/2026, 5:43:07 PM

GitHub: 1,360 stars · 172 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface keirp/automatic_prompt_engineer, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Automates the generation and optimization of prompts for Large Language Models (LLMs) using the Automatic Prompt Engineer (APE) algorithm.
  • highreadme#2
    Reposition the README's opening to highlight the tool's purpose

    Why:

    CURRENT
    # Large Language Models Are Human-Level Prompt Engineers

Yongchao Zhou*, Andrei Ioan Muresanu*, Ziwen Han*, Keiran Paster, Silviu Pitis, Harris Chan, Jimmy Ba

Project Page | ArXiv
| Colab
| Demo

This repo contains code for "Large Language Models Are Human-Level Prompt Engineers". Please see our
paper and project page for more results.
    COPY-PASTE FIX
    # Automatic Prompt Engineer (APE): Automating LLM Prompt Generation and Optimization

This repository provides the official code for the Automatic Prompt Engineer (APE) algorithm, which automates the generation and selection of prompts for Large Language Models (LLMs). It implements the methods from our paper "Large Language Models Are Human-Level Prompt Engineers".

Project Page | ArXiv | Colab | Demo

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface keirp/automatic_prompt_engineer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PromptPerfect
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PromptPerfect · recommended 2×
  2. LangChain · recommended 2×
  3. LlamaIndex · recommended 2×
  4. OpenAI Evals · recommended 1×
  5. Weights & Biases Prompts · recommended 1×
  • CATEGORY QUERY
    How can I automatically generate and optimize prompts for large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Evals
    2. PromptPerfect
    3. LangChain
    4. LlamaIndex
    5. Weights & Biases Prompts
    6. DSPy
    7. Argilla
    8. Label Studio
    9. TRL

    AI recommended 9 alternatives but never named keirp/automatic_prompt_engineer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help improve large language model task performance by optimizing prompts?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Playground / API
    2. LangChain
    3. LlamaIndex
    4. W&B Prompts
    5. Humanloop
    6. Guardrails AI
    7. PromptPerfect

    AI recommended 7 alternatives but never named keirp/automatic_prompt_engineer. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of keirp/automatic_prompt_engineer?
    pass
    AI named keirp/automatic_prompt_engineer explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts keirp/automatic_prompt_engineer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named keirp/automatic_prompt_engineer explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo keirp/automatic_prompt_engineer solve, and who is the primary audience?
    pass
    AI named keirp/automatic_prompt_engineer explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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keirp/automatic_prompt_engineer — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite