RRepoGEO

REPOGEO REPORT · LITE

BeachWang/DAIL-SQL

Default branch main · commit 2061f681 · scanned 6/11/2026, 9:32:53 PM

GitHub: 635 stars · 94 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 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 BeachWang/DAIL-SQL, 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
  • highreadme#1
    Add a clear positioning statement to the README introduction

    Why:

    CURRENT
    # DAIL-SQL
    
    DAIL-SQL is a highly effective and efficient approach for optimizing the utilization of LLM on Text-to-SQL. It has proven its superiority by achieving a remarkable score of 86.2% on the Spider leaderboard using GPT-4 during testing. Notably, it only requires approximately 1600 tokens per question in Spider-dev. In addition to this, we have achieved an even higher score of 86.6% on Spider-test through self-consistency voting of GPT-4.
    COPY-PASTE FIX
    # DAIL-SQL: An Efficient Few-Shot NL2SQL Method for LLMs
    
    DAIL-SQL is a highly effective and efficient *method and framework* for optimizing the utilization of Large Language Models (LLMs) on Text-to-SQL tasks. It provides a robust approach for few-shot Natural Language to SQL generation, achieving a remarkable 86.2% on the Spider leaderboard using GPT-4. This repository offers the code and resources to implement and reproduce our state-of-the-art results, demonstrating a practical solution for high-accuracy NL2SQL.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2308.15363

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 BeachWang/DAIL-SQL
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google's T5-large
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google's T5-large · recommended 1×
  2. Meta's CodeLlama · recommended 1×
  3. defog-ai/sqlcoder · recommended 1×
  4. GPT-4 · recommended 1×
  5. Claude 3 Opus · recommended 1×
  • CATEGORY QUERY
    Seeking a highly accurate and efficient text-to-SQL solution leveraging large language models.
    you: not recommended
    AI recommended (in order):
    1. Google's T5-large
    2. Meta's CodeLlama
    3. SQLCoder (defog-ai/sqlcoder)
    4. GPT-4
    5. Claude 3 Opus
    6. DB-GPT (eosphoros-ai/DB-GPT)
    7. Dataherald

    AI recommended 7 alternatives but never named BeachWang/DAIL-SQL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to optimize large language model performance for few-shot natural language to SQL tasks?
    you: not recommended
    AI recommended (in order):
    1. Spider
    2. Picard
    3. RAT-SQL
    4. SmBoP
    5. DIN-SQL
    6. GraPPa

    AI recommended 6 alternatives but never named BeachWang/DAIL-SQL. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 BeachWang/DAIL-SQL?
    pass
    AI named BeachWang/DAIL-SQL explicitly

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

  • If a team adopts BeachWang/DAIL-SQL in production, what risks or prerequisites should they evaluate first?
    pass
    AI named BeachWang/DAIL-SQL 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 BeachWang/DAIL-SQL solve, and who is the primary audience?
    pass
    AI named BeachWang/DAIL-SQL explicitly

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

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BeachWang/DAIL-SQL — 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