{"query":"Enterprise Playbooks","corpusVersion":"local","generatedAt":"2026-09-13T04:34:27.024Z","results":[{"blockId":"playbooks.overview#enterprise-playbooks","pageId":"playbooks.overview","title":"Enterprise Playbooks","pageTitle":"Enterprise Playbooks","url":"https://teammately.ai/docs/playbooks.md","humanUrl":"https://teammately.ai/docs/playbooks#enterprise-playbooks","markdownUrl":"https://teammately.ai/docs/playbooks.md","sectionId":"enterprise-playbooks","kind":"concept","productArea":"playbooks","score":1222.546619876206,"reasons":["search_match","title_match","display_title_match","term_match"],"markdown":"# Enterprise Playbooks\n\nPlaybooks are scenario recipes for applying Teammately's correctness lifecycle to product-specific AI risks. They do not introduce separate product surfaces; they connect existing artifacts such as Cases, Expert Contributions, Policies, Rubrics, coverage, Benchmark Evaluations, and improvement evidence."},{"blockId":"playbooks.overview#choose-a-playbook","pageId":"playbooks.overview","title":"Choose a playbook","pageTitle":"Enterprise Playbooks","url":"https://teammately.ai/docs/playbooks.md","humanUrl":"https://teammately.ai/docs/playbooks#choose-a-playbook","markdownUrl":"https://teammately.ai/docs/playbooks.md","sectionId":"choose-a-playbook","kind":"concept","productArea":"playbooks","score":488.5024978467948,"reasons":["search_match","page_title_match","term_match","prefix_or_fuzzy_match"],"markdown":"## Choose a playbook\n\n| Starting problem | Playbook |\n| --- | --- |\n| Retrieved sources, citation, abstention, or answer grounding | [RAG correctness benchmark](/docs/playbooks/building-correctness-benchmark-rag) |\n| Search intent, source authority, document conflicts, or freshness | [Enterprise search](/docs/playbooks/enterprise-search) |\n| Refunds, commitments, account context, or escalation | [Customer support AI](/docs/playbooks/customer-support-ai) |\n| Many rules, exceptions, or controlled source hierarchies | [Policy-heavy AI systems](/docs/playbooks/policy-heavy-ai-systems) |\n| A bounded question requires accountable specialist judgment | [Run Expert Contributions](/docs/playbooks/running-expert-contributions-enterprise-assistant) |\n| Contribution evidence needs to become reusable standards | [Turn judgment into Policies and Rubrics](/docs/playbooks/turning-expert-judgment-into-policies-and-rubrics) |\n| Qualified experts disagree | [Handle conflicting opinions](/docs/playbooks/handling-conflicting-expert-opinions) |\n| Important behavior may be absent from the selected Dataset | [Find coverage gaps](/docs/playbooks/finding-coverage-gaps-before-review) |\n| New evidence or a changed rule makes the current boundary stale | [Refresh a Benchmark](/docs/playbooks/refreshing-a-benchmark-from-new-signals) |\n| CI or another evaluation system already owns execution facts | [Use existing evaluation infrastructure](/docs/playbooks/using-teammately-alongside-existing-evaluation-infrastructure) |"},{"blockId":"playbooks.enterprise-search#using-teammately-for-enterprise-search","pageId":"playbooks.enterprise-search","title":"Using Teammately for Enterprise Search","pageTitle":"Using Teammately for Enterprise Search","url":"https://teammately.ai/docs/playbooks/enterprise-search.md","humanUrl":"https://teammately.ai/docs/playbooks/enterprise-search#using-teammately-for-enterprise-search","markdownUrl":"https://teammately.ai/docs/playbooks/enterprise-search.md","sectionId":"using-teammately-for-enterprise-search","kind":"recipe","productArea":"playbooks","score":441.2069052191066,"reasons":["search_match","term_match","prefix_or_fuzzy_match"],"markdown":"# Using Teammately for Enterprise Search\n\nUse this playbook when an enterprise search or answer system must respect intent, source authority, document freshness, and uncertainty."},{"blockId":"playbooks.enterprise-assistant-review#running-expert-contributions-for-an-enterprise-assistant","pageId":"playbooks.enterprise-assistant-review","title":"Running Expert Contributions for an Enterprise Assistant","pageTitle":"Running Expert Contributions for an Enterprise Assistant","url":"https://teammately.ai/docs/playbooks/running-expert-contributions-enterprise-assistant.md","humanUrl":"https://teammately.ai/docs/playbooks/running-expert-contributions-enterprise-assistant#running-expert-contributions-for-an-enterprise-assistant","markdownUrl":"https://teammately.ai/docs/playbooks/running-expert-contributions-enterprise-assistant.md","sectionId":"running-expert-contributions-for-an-enterprise-assistant","kind":"recipe","productArea":"playbooks","score":368.35292421953847,"reasons":["search_match","term_match","prefix_or_fuzzy_match"],"markdown":"# Running Expert Contributions for an Enterprise Assistant\n\nUse this playbook when an enterprise assistant needs domain experts to judge outputs before the team turns that judgment into standards and benchmarks."},{"blockId":"playbooks.overview#definition","pageId":"playbooks.overview","title":"Definition","pageTitle":"Enterprise Playbooks","url":"https://teammately.ai/docs/playbooks.md","humanUrl":"https://teammately.ai/docs/playbooks#definition","markdownUrl":"https://teammately.ai/docs/playbooks.md","sectionId":"definition","kind":"concept","productArea":"playbooks","score":348.1589125187747,"reasons":["search_match","page_title_match","term_match"],"markdown":"## Definition\n\nEnterprise Playbooks names the scenario layer of the docs. Use it when a team knows the kind of AI system or operating problem it has, but needs a concrete path through Teammately's existing correctness artifacts."},{"blockId":"playbooks.overview#why-it-matters","pageId":"playbooks.overview","title":"Why it matters","pageTitle":"Enterprise Playbooks","url":"https://teammately.ai/docs/playbooks.md","humanUrl":"https://teammately.ai/docs/playbooks#why-it-matters","markdownUrl":"https://teammately.ai/docs/playbooks.md","sectionId":"why-it-matters","kind":"concept","productArea":"playbooks","score":345.75970404797766,"reasons":["search_match","page_title_match","term_match"],"markdown":"## Why it matters\n\nEnterprise AI teams often begin with examples, external logs, reviewer comments, or model outputs before they have explicit correctness standards. Playbooks route that material into current Teammately surfaces and state the decision gates that must be satisfied before evidence is trusted."},{"blockId":"playbooks.overview#where-it-appears-in-the-product","pageId":"playbooks.overview","title":"Where it appears in the product","pageTitle":"Enterprise Playbooks","url":"https://teammately.ai/docs/playbooks.md","humanUrl":"https://teammately.ai/docs/playbooks#where-it-appears-in-the-product","markdownUrl":"https://teammately.ai/docs/playbooks.md","sectionId":"where-it-appears-in-the-product","kind":"concept","productArea":"playbooks","score":335.2046226804377,"reasons":["search_match","page_title_match","term_match"],"markdown":"## Where it appears in the product\n\nLook for playbooks in this section of the docs. Product screens use operational labels for Cases, Expert Contributions, Policies, Rubrics, coverage, Benchmark Evaluations, and Improve; playbooks organize those existing surfaces around common scenarios."},{"blockId":"playbooks.overview#artifacts-it-affects","pageId":"playbooks.overview","title":"Artifacts it affects","pageTitle":"Enterprise Playbooks","url":"https://teammately.ai/docs/playbooks.md","humanUrl":"https://teammately.ai/docs/playbooks#artifacts-it-affects","markdownUrl":"https://teammately.ai/docs/playbooks.md","sectionId":"artifacts-it-affects","kind":"concept","productArea":"playbooks","score":327.17054419948374,"reasons":["search_match","page_title_match","term_match","prefix_or_fuzzy_match"],"markdown":"## Artifacts it affects\n\nDepending on the scenario, a playbook can affect imported Cases, supported reference responses, Contribution records, Policies, applicability, Rubrics, Coverage Facets, Dataset Snapshots, Benchmark Versions, Evaluation Runs, comparisons, Improvement Sessions, or customer-owned human review context.\n\n{% example-demo title=\"Choosing a scenario path\" %}\nA support team with refund-policy failures should start with the customer support or policy-heavy system playbook. A search team with stale-source issues should start with the RAG or enterprise search playbook. A team that already has CI metrics should start with the existing-evaluation-infrastructure playbook.\n\nThe common thread is the same: turn human judgment and source context into explicit standards, cover the risky behavior slices, and use Benchmark Evaluations to produce benchmark interpretation grounded in real results.\n{% /example-demo %}"}]}