How often is docs.smith.langchain.com cited by AI models?

docs.smith.langchain.com was cited 2 times by AI models, representing 0.3% of all source citations.

Which AI models cite docs.smith.langchain.com?

docs.smith.langchain.com is cited by 1 AI models including Perplexity Sonar (with search) (latest), Anthropic Claude Sonnet (latest), OpenAI ChatGPT Latest, Google Gemini Flash (latest), X.AI Grok (latest).

What is docs.smith.langchain.com's ranking among source domains?

docs.smith.langchain.com ranks #60 out of 266 source domains analyzed (with 183 brands also tracked).

What brands are associated with docs.smith.langchain.com?

AI models reference docs.smith.langchain.com when discussing brands like OpenAI.

How is source ranking determined?

Sources are ranked by Share of Voice = Model Coverage × Quality Score. Model weights are based on Monthly Active Users (OpenAI ChatGPT Latest: 900M MAU, Google Gemini Flash (latest): 650M MAU, Perplexity Sonar (with search) (latest): 45M MAU, etc.). Sources appearing earlier in AI responses receive higher prominence scores.

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Share of Voice

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About docs.smith.langchain.com (AI Tools)

This page provides details about docs.smith.langchain.com (AI Tools) which was ranked #60 out of 266 in the list of sources (2 citations (0.3% 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.

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Rank

Voice

Position

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Citations

Used by AI Models

Model	Voice	Position	Citations
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Pages from docs.smith.langchain.com (1 link)

URL Path	Citations	Position
docs.smith.langchain.com	2	154

Brands Referenced By This Website (1 brand)

Brand	Voice	Mentions
OpenAI	17.6%	43

Citations from AI Responses

OpenAI ChatGPT Latest (2 citations)

What are the proven best practices and strategies experts use for AI Tools for Marketers?

[...] cs/) - 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/phoenix](https://github.com/Arize-ai/phoenix) 7) Governance, legal, and brand safety [...]

docs.smith.langchain.com October 24, 2025

[...] ndexes and prompts. - Common data platforms: BigQuery [https://cloud.google.com/bigquery](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 “hum [...]