'application' 검색 결과

Design and refine prompts for LLM applications by organizing roles, context, instructions, constraints, and output formats. Develop representative examples, manage the information supplied to the model, and evaluate changes to improve how consistently responses match expectations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Structuring a system prompt for an LLM application
• Specifying response formats and constraints
• Creating examples that include edge cases

Evaluate LLM application quality using automated scores, human reviews, and another language model as a judge. Compare models or prompts, establish baseline results, and detect quality drops through repeatable tests and statistical comparisons.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing models and prompts
• Checking quality before deployment
• Evaluating search quality in RAG applications

Design LLM applications using patterns for answers grounded in documents, agents that use tools, and prompt testing. Implementation examples also cover monitoring response quality, latency, costs, and failures during operation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an assistant that answers from internal documents
• Choosing an architecture for an agent that uses tools
• Comparing and versioning prompts

Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

Build LlamaIndex applications that retrieve relevant material before generating answers, a method called RAG. Connect documents, websites, and databases to support document questions, conversational search, structured summaries, and checks of answer relevance and source consistency.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building question answering over internal documents
• Creating a chatbot that remembers the conversation
• Searching information across files, websites, and databases

Build Zoom meeting integrations in Windows C++ applications, including custom interfaces and bots that run without a visible interface. Guidance covers authentication, audio and video data access, project setup, and troubleshooting callbacks through the Windows message loop.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Embedding Zoom meetings in a Windows desktop application
• Building a meeting bot without a visible interface
• Receiving meeting audio and video data

Find suitable grants and bidding opportunities, check eligibility before drafting, and prepare applications or responses to requests for proposals using past submissions and actual organizational data. It also tracks submission deadlines and reporting obligations after an award.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding grants your organization qualifies for
• Screening solicitations before deciding to bid
• Drafting applications from past submissions

Turn applications into a ranked shortlist using only the job’s stated criteria, with evidence for each score. Prepare candidate replies, coordinate interview times, and create an onboarding checklist while leaving hiring decisions to the owner.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Shortlisting applicants against the job’s stated criteria
• Drafting interview invitations and rejection replies
• Coordinating interview times

Build and update Mastra application interfaces using existing components from @mastra/playground-ui. Follow shared visual styles, arrange layouts with Tailwind utilities, and connect themes and stylesheets while avoiding duplicated components or unsupported appearance overrides.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a new Mastra application screen
• Adjusting layouts with existing components
• Connecting shared themes and stylesheets

Read and operate desktop applications through their interfaces, including clicks, text entry, menus, and scrolling. Use it to inspect or edit app content when a dedicated connector, API, or command-line tool cannot handle the task.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspect information displayed in a desktop app
• Enter or edit text in an application
• Operate menus and dialogs

Create a functional application prototype from an initial idea. The workflow covers requirements, technology choices, plan approval, project setup, implementation, visual assets where needed, build verification, and instructions for starting the result.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a website prototype from an idea
• Creating an initial mobile or desktop application
• Developing a playable 2D or 3D game prototype

Guide a TanStack Table migration from v8 to v9 in Lit applications. Review table construction, rendering helpers, state updates, and shared application hooks, alongside the required migration checks for the core library.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Migrate a Lit table from v8 to v9
• Update TableController construction and render calls
• Review state updates during migration

Design and implement Google Cloud serverless web apps with separate presentation, application, and data layers. Produces architecture diagrams, Terraform infrastructure code, deployment instructions, and validation scripts for private networking and controlled access between layers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design a serverless web app with separate application layers
• Generate Terraform for private Cloud Run and Cloud SQL connections
• Plan regional data residency requirements

Guides authentication and access permissions for Google Cloud users and applications. It helps choose credentials for local development, production services, or external clouds, with guidance on short-lived credentials and avoiding downloaded service account keys.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up credentials for local Python development
• Configure Cloud Run access to Cloud SQL
• Connect workloads on AWS or Azure to Google Cloud

Audit and strengthen security for applications running on GKE. Configure pod traffic restrictions, sandbox isolation, namespace security standards, and Secret Manager volume mounts to protect workloads at the application and namespace level.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing GKE security settings
• Restricting communication within a namespace
• Isolating risky pods and applying security standards

Adjust application capacity on GKE through manual scaling, HPA for changing the number of running pods, and VPA for adjusting CPU and memory allocations. Includes configuration templates and a workflow for reviewing resource requests against actual usage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Scale application capacity automatically as demand changes
• Review CPU and memory allocations against actual usage
• Change the number of application replicas manually

Configure how GKE applications receive traffic from the internet or internal networks. The skill provides configuration templates for routing, load balancing, certificates, and access protection, and guides diagnosis of failed health checks, dropped requests, and TLS connection problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Expose a GKE application through Gateway API or Ingress
• Split traffic between application versions during a rollout
• Configure Cloud Armor protection and HTTPS certificates

Deploy and manage applications on Cloud Run using source code or container images. Choose services for web requests, jobs for tasks that finish, or worker pools for continuous background processing, with guidance on execution settings and deployment failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploy a web application from source code or a container
• Create and run batch jobs with parallel tasks
• Deploy continuous queue-processing workers

Add DeepEval tracing to Python AI applications so execution steps appear in Confident AI. Choose a supported integration or manual @observe instrumentation, then capture inputs, outputs, step types, tags, and metadata.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Trace execution steps in a Python AI application
• Select tracing integrations for an existing framework
• Add manual tracing with @observe

Build multi-page Next.js applications from JSON specifications. This skill guides route setup, shared layouts, page metadata, and server-side rendering, which prepares pages on the server, with optional data loading before display.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a multi-page app from JSON specifications
• Share navigation and layouts across pages
• Load server data before rendering a page

Helps deploy, manage, and troubleshoot applications on Kubernetes using kubectl and Helm. Covers container logs, resource settings, network access, and deployment strategies to investigate failures and review how applications run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating repeatedly crashing containers
• Reviewing deployment and resource settings
• Troubleshooting service connectivity and DNS

Manage recruiting in Ashby with natural language requests from Claude Code. Create and update candidates, create jobs and applications, review interview schedules and candidate notes, and combine records to report on hiring progress.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Registering candidates and updating contact details
• Creating jobs and linking candidates to applications
• Reviewing interview schedules and candidate notes

Test local web applications with Python scripts using Playwright, a browser automation tool. Inspect rendered pages, exercise interface controls, capture screenshots and console logs, and use a helper to manage one or more application servers during tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verifying buttons, links, and form inputs in a local app
• Investigating unexpected interface behavior
• Capturing screenshots and browser console logs

Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments