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About github.com (Developer Tools)
This page provides details about github.com (Developer Tools) which was ranked #5 out of 266 in the list of sources (13 citations (3.3% share)) in answers from AI models (OpenAI ChatGPT Latest) when they were asked the following 2 questions: "What are the proven best practices and strategies experts use for AI Tools for Marketers?", "What do industry leaders recommend as the first steps when starting with AI Tools for Marketers?" on Oct 24, 2025 by AI Chat Watch. This source is referenced for brands: OpenAI, Anthropic, Microsoft, Google, OWASP Top 10 for LLM Applications.
Used by AI Models
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Pages from github.com (8 links)
| URL Path | Citations | Position |
|---|---|---|
| /google/lightweight_mmm | 3 | 9 |
| /facebookexperimental/Robyn | 2 | 13 |
| /openai/evals | 2 | 61 |
| /facebookresearch/faiss | 1 | 80 |
| /openai/openai-cookbook | 1 | 106 |
| /explodinggradients/ragas | 1 | 157 |
| /Arize-ai/phoenix | 2 | 158 |
| /Trusted-AI/AIF360 | 1 | 163 |
Brands Referenced By This Website (5 brands)
| Brand | Voice | Mentions |
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58.6% | 104 |
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17.6% | 43 |
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9.7% | 23 |
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4.9% | 15 |
| OWASP Top 10 for LLM Applications | 0.8% | 3 |
Citations from AI Responses
OpenAI ChatGPT Latest
(13 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:// [...]
What do industry leaders recommend as the first steps when starting with AI Tools for Marketers?
[...] - Run a 2–4 week pilot with clear success criteria and measurement. Compare time saved, quality, and downstream performance (e.g., click or conversion lift) against a baseline; keep a human-in-the-loop for review; document findings: - OpenAI Evals (example frameworks for systematic evaluation) (https://github.com/openai/evals) - OWASP Top 10 for LLM Applications (use as a quick risk/abuse checklist during pilots) (https://owasp.org/www-project-top-10-for-large-language-model-applications/) - Upskill the team and name an internal AI champion. Provide short, role-based training; publish internal SOPs/playbooks; capture [...]