REPOGEO REPORT · LITE

stanford-oval/WikiChat

Default branch main · commit 803683b1 · scanned 5/22/2026, 5:36:51 PM

GitHub: 1,592 stars · 143 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

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 stanford-oval/WikiChat, 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

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

OVERALL DIRECTION

highreadme#1

Reposition README opening to highlight interactive RAG application

Why:

CURRENT

Stopping the Hallucination of Large Language Models

COPY-PASTE FIX

WikiChat is an interactive, human-in-the-loop RAG application designed to stop large language model hallucination by enabling real-time, conversational Wikipedia querying for fact-checking and information retrieval.

mediumtopics#2

Add more specific topics to clarify interactive RAG application

Why:

CURRENT

chatbot, emnlp2023, factuality, llm, natural-language-processing, nlp, rag

COPY-PASTE FIX

chatbot, emnlp2023, factuality, llm, natural-language-processing, nlp, rag, interactive-ai, conversational-ai, human-in-the-loop

mediumreadme#3

Add a 'Comparison to RAG Frameworks' section in the README

Why:

COPY-PASTE FIX

Add a new section, for example: `# Comparison to RAG Frameworks` followed by text explaining how WikiChat differs from general RAG libraries like LangChain or LlamaIndex by focusing on interactive, human-guided fact-checking and real-time Wikipedia querying, rather than being a foundational framework for building RAG systems.

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 stanford-oval/WikiChat

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

langchain-ai/langchain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

langchain-ai/langchain · recommended 2×
run-llama/llama_index · recommended 2×
Pinecone · recommended 1×
weaviate/weaviate · recommended 1×
chroma-core/chroma · recommended 1×

CATEGORY QUERY
How to prevent large language models from hallucinating in conversational AI applications?
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. Pinecone
4. Weaviate (weaviate/weaviate)
5. Chroma (chroma-core/chroma)
6. OpenAI API Fine-tuning
7. Hugging Face Transformers (huggingface/transformers)
8. AWS SageMaker
9. Google Cloud Vertex AI
10. GPT-4
11. Neo4j (neo4j/neo4j)
AI recommended 11 alternatives but never named stanford-oval/WikiChat. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a robust retrieval-augmented generation framework to enhance LLM factuality and reduce errors.
you: not recommended
AI recommended (in order):
1. LlamaIndex (run-llama/llama_index)
2. LangChain (langchain-ai/langchain)
3. Haystack (deepset-ai/haystack)
4. RAGatouille (matsch/RAGatouille)
5. DSPy (stanfordnlp/dspy)
6. OpenAI Assistants API
AI recommended 6 alternatives but never named stanford-oval/WikiChat. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
pass
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 stanford-oval/WikiChat?
pass
AI named stanford-oval/WikiChat explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts stanford-oval/WikiChat in production, what risks or prerequisites should they evaluate first?
pass
AI named stanford-oval/WikiChat 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 stanford-oval/WikiChat solve, and who is the primary audience?
pass
AI named stanford-oval/WikiChat explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of stanford-oval/WikiChat. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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stanford-oval/WikiChat — 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