「rag」の検索結果

Account assignment by revenue potential, geography, relationship. Workload balancing, TAM/SAM calculation, coverage models.

日本語の概要は準備中です。原文の説明を表示しています。

Drop a folder of vendor quotes, proposals, and SOWs on Cowork -- normalizes them into one comparison matrix, computes true total cost of ownership, surfaces the terms each vendor buried, and arms you with negotiation leverage points and reference-check questions.

日本語の概要は準備中です。原文の説明を表示しています。

Generates a comprehensive client health overview across all accounts. Reads CRM data, support tickets, usage metrics, billing, and engagement logs. Calculates health scores, trend direction, and RAG status per client. Outputs a sorted risk report with recommended actions.

日本語の概要は準備中です。原文の説明を表示しています。

Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.

日本語の概要は準備中です。原文の説明を表示しています。

Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks.

日本語の概要は準備中です。原文の説明を表示しています。

Unity Catalog governance, access control, and observability. Use to grant or revoke access (GRANT/REVOKE), reason about the privilege model and ownership, set up row-level security and column masks, create external locations and storage credentials, define catalogs/schemas/tables/volumes, answer "who can read this table", and query system tables (audit, lineage, billing) or work with volume files in /Volumes/.

日本語の概要は準備中です。原文の説明を表示しています。

Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).

日本語の概要は準備中です。原文の説明を表示しています。

Fix a reported bug with a regression test that demonstrably fails on the original behavior and passes after the fix. Use when implementing a bug fix or checking whether a proposed fix actually addresses a reported failure. Not a general coverage audit or new-feature workflow.

日本語の概要は準備中です。原文の説明を表示しています。

Find consequential behavior that a passing test suite does not establish, using focused exploratory checks. Use when the user asks what green tests miss or wants confidence in test coverage for a specific change. Not a general code review, routine test run, or debugging workflow for an already failing test.

日本語の概要は準備中です。原文の説明を表示しています。

Test-driven development workflow with test generation, coverage analysis, and multi-framework support

日本語の概要は準備中です。原文の説明を表示しています。

Use when a medical AI paper should be found and cited by AI search engines and RAG tools. Optimizes the title, abstract, summary box (Key Points, Research in Context), keywords, preprint, GitHub README/CITATION.cff and Hugging Face card, returning a visible pass/fail checklist.

日本語の概要は準備中です。原文の説明を表示しています。

Analyzes code change impact with risk scoring and affected-node mapping. Use before merging to understand what a change touches and what lacks test coverage.

日本語の概要は準備中です。原文の説明を表示しています。

Evaluates and improves skills, agents, commands, and hooks after a workflow slice. Use when execution felt slow, confusing, repetitive, or fragile.

日本語の概要は準備中です。原文の説明を表示しています。

Builds session-scoped temporary memory palaces for extended conversations. Use when tracking state across interruptions in a multi-step project.

日本語の概要は準備中です。原文の説明を表示しています。

Updates, generates, and validates tests using git-workspace context and TDD/BDD methodology. Use when code changes require new or updated test coverage.

日本語の概要は準備中です。原文の説明を表示しています。

Audits the DSA problem bank for coverage gaps and proposes new YAML entries. Use when refreshing the problem bank during update-plugins runs.

日本語の概要は準備中です。原文の説明を表示しています。

Guide minimal code via a decision ladder with full safety, edge, and negative-case coverage. Use when adding code, choosing a dependency, or auditing a diff.

日本語の概要は準備中です。原文の説明を表示しています。

Enforce evidence bar, coverage gates, and regression guards. Use when adding tests or claiming done. Do not use to run suites; use night-market-operations.

日本語の概要は準備中です。原文の説明を表示しています。

Use for Feast feature store tasks: feature repositories, definitions, CLI, retrieval, materialization, serving, RAG/vector search, integrations, and Feast contributor workflows.

日本語の概要は準備中です。原文の説明を表示しています。

Use Galaxy repository guidance for server configuration, APIs, tool and workflow development, data/storage, Tool Shed operations, and web-client development.

日本語の概要は準備中です。原文の説明を表示しています。

Use DocArray for multimodal Pydantic-style documents, typed DocList and DocVec batches, serialization and local storage, FastAPI payloads, and vector retrieval indexes.

日本語の概要は準備中です。原文の説明を表示しています。

Route DeepSearcher workflows for provider setup, data ingestion, RAG querying, CLI and service operation, and evaluation.

日本語の概要は準備中です。原文の説明を表示しています。

Use DB-GPT 0.8.1 as an agentic AI data platform: install and configure provider profiles, operate its CLI and services, build agents and AWEL flows, ingest and retrieve data with RAG, call client/API surfaces, and execute code through explicitly bounded sandbox runtimes.

日本語の概要は準備中です。原文の説明を表示しています。

Operate ChatterBot conversational dialog engine workflows, including chatbot setup, training, logic adapters, storage backends, Django integration, and optional LLM adapters.

日本語の概要は準備中です。原文の説明を表示しています。