{{ domain.value }}
About github.com (Developer Tools)
This page provides details about github.com (Developer Tools) which was ranked #1 out of 118 in the list of sources (12 citations (13.6% share)) in answers from AI models (OpenAI ChatGPT Latest) when they were asked the following 1 question: "What are the proven best practices and strategies experts use for AI Tools for Marketers?" on Oct 24, 2025 by AI Chat Watch. This source is referenced for brands: OpenAI, Anthropic, Microsoft.
Used by AI Models
| Model | Voice | Position | Citations |
|---|---|---|---|
|
|
{{ getInfluenceForBot(bot.id) }} | {{ getPositionForBot(bot.id) }} | {{ getMentionsForBot(bot.id) }} |
Pages from github.com (8 links)
| URL Path | Citations | Position |
|---|---|---|
| /google/lightweight_mmm | 3 | 2 |
| /facebookexperimental/Robyn | 2 | 3 |
| /facebookresearch/faiss | 1 | 19 |
| /openai/openai-cookbook | 1 | 30 |
| /openai/evals | 1 | 53 |
| /explodinggradients/ragas | 1 | 56 |
| /Arize-ai/phoenix | 2 | 57 |
| /Trusted-AI/AIF360 | 1 | 63 |
Citations from AI Responses
OpenAI ChatGPT Latest
(12 citations)
What are the proven best practices and strategies experts use for AI Tools for Marketers?
[...] ://support.google.com/google-ads/answer/9831769](https://support.google.com/google-ads/answer/9831769) - Marketing mix modeling and incrementality methods to quantify impact: Google’s LightweightMMM [https://github.com/google/lightweight_mmm](https://github.com/google/lightweight_mmm), Meta’s Robyn [https://github.com/facebookexperimental/Robyn](https://github.com/facebookexperimental/Robyn), Meta Conversion Lift [https://www.facebook.com/business/help/1158188519646723?id=445653312788501](https://www.facebook.com/business/help/1158188519646723?id=445653312788501) - Industry benchmarking on GenAI adoption/ROI: McKinsey State of AI 2024 [https://www.mckinsey.com/capabilities/quantumblac [...]
[...] zely [https://www.optimizely.com](https://www.optimizely.com), VWO [https://vwo.com](https://vwo.com) - MMM for budget allocation; multi-touch attribution carefully used with bias awareness. LightweightMMM [https://github.com/google/lightweight_mmm](https://github.com/google/lightweight_mmm), Robyn [https://github.com/facebookexperimental/Robyn](https://github.com/facebookexperimental/Robyn) - Analytics and event quality: GA4 [https://marketingplatform.google.com/about/analytics/](https://marketingplatform.google.com/about/analytics/) - Mobile/app attribution when relevant: AppsFlyer [https://www.appsflyer.com](https://www.appsflyer.com), Adjust [https://www.adjust.com](https://www.ad [...]
[...] -generation). Typical stack: LangChain [https://python.langchain.com/docs/](https://python.langchain.com/docs/), LlamaIndex [https://www.llamaindex.ai](https://www.llamaindex.ai), Pinecone [https://www.pinecone.io](https://www.pinecone.io), Weaviate [https://weaviate.io](https://weaviate.io), FAISS [https://github.com/facebookresearch/faiss](https://github.com/facebookresearch/faiss), Azure AI Search [https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) - Fine-tuning vs. prompting: start with prompting + RAG; only fine‑tune where you need style adaptation or domain patterns not s [...]
[...] /elevenlabs.io](https://elevenlabs.io) 4) Operationalize prompt engineering and brand voice - Maintain prompt libraries and instructions that define tone, audience, claims policy, references, and compliance notes; use system messages and guardrails. - Prompt engineering references: OpenAI Cookbook [https://github.com/openai/openai-cookbook](https://github.com/openai/openai-cookbook), Anthropic prompt engineering [https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering) - Reduce hallucinations with grounding, retrieval checks, and citations: Anthropic on reducing hallucinations [https://do [...]
[...] rove lift and ROI before scaling. - See experimentation guidance: Google Ads Experiments (A/B) [https://support.google.com/google-ads/answer/9831769](https://support.google.com/google-ads/answer/9831769) - Marketing mix modeling and incrementality methods to quantify impact: Google’s LightweightMMM [https://github.com/google/lightweight_mmm](https://github.com/google/lightweight_mmm), Meta’s Robyn [https://github.com/facebookexperimental/Robyn](https://github.com/facebookexperimental/Robyn), Meta Conversion Lift [https://www.facebook.com/business/help/1158188519646723?id=445653312788501](https://www.facebook.com/business/help/1158188519646723?id=445653312788501) - Industry ben [...]
[...] e.com/google-ads/answer/9831769](https://support.google.com/google-ads/answer/9831769), Optimizely [https://www.optimizely.com](https://www.optimizely.com), VWO [https://vwo.com](https://vwo.com) - MMM for budget allocation; multi-touch attribution carefully used with bias awareness. LightweightMMM [https://github.com/google/lightweight_mmm](https://github.com/google/lightweight_mmm), Robyn [https://github.com/facebookexperimental/Robyn](https://github.com/facebookexperimental/Robyn) - Analytics and event quality: GA4 [https://marketingplatform.google.com/about/analytics/](https://marketingplatform.google.com/about/analytics/) - Mobile/app attribution when relevant: AppsFlyer [...]
[...] ad copy/asset variants aligned to personas → run split tests in Google Ads Experiments → scale winners → measure incrementality with holdouts/MMM. - Google Ads Experiments [https://support.google.com/google-ads/answer/9831769](https://support.google.com/google-ads/answer/9831769), LightweightMMM [https://github.com/google/lightweight_mmm](https://github.com/google/lightweight_mmm) - Email/CRM: Use AI to create segment-specific versions and subject lines → safety-check claims and compliance → test send times and variants → route insights back to prompts. - HubSpot [https://www.hubspot.com](https://www.hubspot.com), Marketo [https://business.adobe.com/products/marketo/adobe [...]
[...] oogle.com/about/analytics/](https://marketingplatform.google.com/about/analytics/) - Mobile/app attribution when relevant: AppsFlyer [https://www.appsflyer.com](https://www.appsflyer.com), Adjust [https://www.adjust.com](https://www.adjust.com) - LLM-specific evaluation and monitoring: OpenAI Evals [https://github.com/openai/evals](https://github.com/openai/evals), LangSmith [https://docs.smith.langchain.com](https://docs.smith.langchain.com), TruLens [https://www.trulens.org](https://www.trulens.org), Ragas (RAG eval) [https://github.com/explodinggradients/ragas](https://github.com/explodinggradients/ragas), Arize Phoenix [https://github.com/Arize-ai/phoen [...]
[...] s](https://github.com/openai/evals), LangSmith [https://docs.smith.langchain.com](https://docs.smith.langchain.com), TruLens [https://www.trulens.org](https://www.trulens.org), Ragas (RAG eval) [https://github.com/explodinggradients/ragas](https://github.com/explodinggradients/ragas), Arize Phoenix [https://github.com/Arize-ai/phoenix](https://github.com/Arize-ai/phoenix) 7) Governance, legal, and brand safety guardrails - Adopt an AI risk framework; define roles, approvals, and audit trails. NIST AI Risk Management Framework [https://www.nist.gov/ai/risk-management](https://www.nist.gov/ai/risk-management) - Security and prompt-injection defenses for marketing ag [...]
[...] uery](https://cloud.google.com/bigquery), Databricks [https://www.databricks.com](https://www.databricks.com), Snowflake [https://www.snowflake.com](https://www.snowflake.com) - Observability for LLM apps: LangSmith [https://docs.smith.langchain.com](https://docs.smith.langchain.com), Arize Phoenix [https://github.com/Arize-ai/phoenix](https://github.com/Arize-ai/phoenix), TruLens [https://www.trulens.org](https://www.trulens.org) 10) Proven rollout approach - Start with low‑risk, high‑leverage pilots (content variants, support macros, ad copy ideation). Use holdouts and “human in the loop” review. - Build an internal AI Center of Excellence and playbooks; train t [...]
[...] bots-for-gptbot/), Google-Extended control [https://developers.google.com/search/blog/2023/09/introducing-google-extended](https://developers.google.com/search/blog/2023/09/introducing-google-extended) - Fairness and bias checks (esp. for audience selection and personalization): IBM AI Fairness 360 [https://github.com/Trusted-AI/AIF360](https://github.com/Trusted-AI/AIF360), Microsoft Responsible AI resources [https://www.microsoft.com/ai/responsible-ai](https://www.microsoft.com/ai/responsible-ai) 8) Content and creative operations at scale - Create an AI-enabled content pipeline: briefs → outlines → drafts → legal/brand review → fact-check → publish → performance [...]
[...] s://www.adjust.com) - LLM-specific evaluation and monitoring: OpenAI Evals [https://github.com/openai/evals](https://github.com/openai/evals), LangSmith [https://docs.smith.langchain.com](https://docs.smith.langchain.com), TruLens [https://www.trulens.org](https://www.trulens.org), Ragas (RAG eval) [https://github.com/explodinggradients/ragas](https://github.com/explodinggradients/ragas), Arize Phoenix [https://github.com/Arize-ai/phoenix](https://github.com/Arize-ai/phoenix) 7) Governance, legal, and brand safety guardrails - Adopt an AI risk framework; define roles, approvals, and audit trails. NIST AI Risk Management Framework [https://www.nist.gov/ai/risk-management](https:// [...]