'llm' 검색 결과

Manage the credentials Arize uses to call LLM providers such as OpenAI and Anthropic. Create, inspect, update, or delete connections, configure allowed models and authentication methods, and retrieve connection IDs for use in evaluations.

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• Connect an LLM provider to Arize
• Review existing connections and allowed models
• Rotate stored provider API keys

Set up repeatable security tests for LLM apps with Promptfoo and DeepTeam. Connect them to automated build pipelines, detect renewed vulnerabilities to malicious instructions or data leakage, and compare results across model or prompt changes.

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• Running automated attack tests on every code change
• Blocking merges when security tests fail
• Comparing security across model or prompt versions

Build and operate LLM applications with guidance on RAG, which uses retrieved documents to inform answers. Covers document indexing, vector database selection, prompt improvement, response evaluation, caching, and estimates of model usage costs.

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• Building an assistant that answers from documents
• Choosing a vector database for document search
• Comparing answer quality after prompt changes

Deploy language model APIs and run batch generation with vLLM. Configure GPU memory use, quantization—lower precision model storage—and distribution across GPUs, then test response times and monitor how many requests the server handles.

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• Deploying an OpenAI-compatible language model API
• Generating responses for a large set of prompts
• Fitting a large model into limited GPU memory

Review pending submissions in NLA escrow arrangements, where assets are held until conditions are met. Compare submissions with demands, approve or reject them interactively or through an LLM, and record arbitration decisions on the blockchain.

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• Finding all pending arbitration requests
• Reviewing and deciding submissions for a specific escrow
• Automatically arbitrating pending submissions with an LLM

Audits any domain's AI-readiness by using curl to directly probe robots.txt, llms.txt, and llms-full.txt, then scores each file against a structured checklist and delivers a formatted report with pass/warn/fail findings and actionable fixes. Use this skill whenever a user provides a domain or URL and wants to know if llms.txt or llms-full.txt is available, discoverable, or properly structured. Trigger on phrases like "check llms.txt for", "does this site have llms.txt", "find llms.txt", "check llms for this url", "audit llms.txt", "is llms-full.txt available", or any time a user shares a domain/docs URL and wants AI-readiness checked. Also trigger when the user wants to verify GEO/AEO readiness of a documentation site.

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Generate or audit an `/llms.txt` file at the site root that makes the site legible to LLMs and AI answer engines at inference time, following the llms.txt proposal (Jeremy Howard, 2024). Use this skill whenever the user asks to "build llms.txt", "create llms.txt", "generate llms.txt", "audit llms.txt", "make my site LLM-friendly", "expose the site to AI assistants", "let ChatGPT / Claude / Perplexity cite my site", "add an AI assistants index", or asks about `.md` companion pages for LLM consumption. Output is a valid `llms.txt` file (or an audit) plus optional `.md` companion page generation for top pages.

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Assess tasks before execution to identify transformations eligible to skip LLM calls. Report the eligible share, recommended models for remaining tasks, and estimated upper costs, then save the routing results for later cost reports.

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• Assess similar tasks before a batch run
• Review LLM spending on simple code transformations
• Check the share eligible for Agent Booster

Audit how a brand appears in AI search answers and identify improvements to its content and website access. Review citation patterns, clear answer structure, supporting evidence, and whether AI systems can retrieve key information.

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• Check brand citations across AI search services
• Improve existing articles for clearer answers
• Investigate why AI systems cannot read a site

Evaluate LLM applications using automated scores, human reviews, and model-based judging. Build repeatable checks to compare models or prompts, assess answer quality, and detect declines in performance before deployment.

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• Comparing models and prompts
• Checking quality after prompt changes
• Detecting performance declines before deployment

Add Arize AX tracing to an LLM application, recording how model calls and tool executions connect. Inspect the existing code, select suitable integrations, add instrumentation, and verify that the application runs and traces reach Arize.

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• Setting up tracing for an LLM application
• Recording tool inputs and outputs in an agent
• Adding Arize export to an existing OpenTelemetry setup

Build safety checks for LLM prompts and responses using Meta's LlamaGuard moderation model. Classify potentially harmful content, block flagged messages, serve checks through an API, and integrate them with NeMo Guardrails.

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• Checking prompts before sending them to an LLM
• Filtering responses before displaying them
• Building a moderation API

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.

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

Helps adapt LLMs to specific tasks with LLaMA-Factory, including training through a web interface and memory-saving methods such as QLoRA. Provides documentation references for learning features, implementing workflows, and investigating code problems.

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• Exploring LLM fine-tuning through a web interface
• Checking LoRA and QLoRA training options
• Investigating LLaMA-Factory implementation problems

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

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• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

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• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Launch a new OpenClaw, PicoClaw, or Ottie deployment through Cloud Claw using the local CLI. Configure the agent type, deployment name, model, and Telegram settings according to the supported launch options and requirements.

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• Launch a new OpenClaw deployment
• Deploy PicoClaw or Ottie for Telegram
• Choose an OpenClaw model before launch

[omh] LLM-powered feature to build: LLM app development: prepare a build handoff for an LLM-powered feature with a pinned provider boundary, schema-first outputs, versioned prompt files, grounded retrieval, and an eval suite as a shipped deliverable. Use when the user says: llm-app-dev, llm app development, llm application development, build an llm app, build an llm feature, llm feature development, build a rag pipeline, rag pipeline.

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반복하는 LLM API 요청에서 공통 지시와 자료를 캐시로 재사용하기 쉽게 배치합니다. 캐시 사용량, 읽기·쓰기·저장 비용과 응답 시간을 확인해 구성을 검토합니다.

• 반복 API 요청의 공통 지시를 재배치합니다
• 캐시 읽기·쓰기·저장 비용을 계산합니다
• 캐시 사용률과 응답 시간을 측정합니다

기존 웹 앱에 자연어로 화면을 조작하는 AI 도우미를 추가하도록 돕습니다. 설치와 조작 패널 표시부터 LLM 연결, 동작 확인, 정식 배포 시 설정까지 안내합니다.

• 업무 화면을 글로 조작합니다
• 기존 웹 앱에 AI 도우미를 추가합니다
• 실제 화면으로 제품 시연을 만듭니다

Caveman Cloud의 비용, 응답 시간, 오류를 읽기 전용으로 조사합니다. 집계와 개별 실행 기록을 대조해 지출 내역과 개선 여지를 근거와 함께 정리합니다.

• LLM 비용을 높이는 처리 파악
• 응답 지연과 오류 조사
• 실측 비용과 검증된 절감액 정리

Generate RuVLLM settings for local language model inference and create MicroLoRA or SONA instances. Adjust their small internal weights using quality scores as feedback, with instances held in server memory for subsequent adaptation calls.

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• Configure local language model inference
• Prepare inference tools for an air-gapped environment
• Adjust MicroLoRA using quality feedback

Prepare an evaluation framework before fine-tuning a language model. Build labeled examples, checks for each failure type, and a base-model benchmark, while calibrating model-based judges against human labels to support comparisons after training.

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• Build an evaluation set from agent traces
• Create separate checks for each failure type
• Calibrate an LLM judge against human labels

Set up Phoenix tracing for LLM applications in Python or TypeScript. The skill guides automatic and custom recording of individual operations, conversation grouping, metadata, feedback, and production settings such as batching and personal data masking.

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• Set up tracing for an LLM application
• Record document retrieval and tool calls
• Group conversation traces by session