'llm' 검색 결과

양식, 청구서, 문제집처럼 일정한 형식의 텍스트에서 정보를 추출하는 흐름을 설계합니다. 정규 표현식으로 추출한 뒤 신뢰도가 낮은 항목만 LLM으로 확인하고 수정하도록 안내합니다.

• 문제 본문과 선택지 및 정답 추출
• 양식과 청구서 추출 절차 설계
• 정규 표현식과 LLM 중 선택

Add checks to LLM prompts and responses using Llama Guard 3, NeMo Guardrails, and LLM Guard. Configure content screening, conversation boundaries, and sensitive-data filtering, then measure blocking and false-positive rates with labeled test prompts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding runtime safety checks to LLM, RAG, or agent apps
• Screening prompts for injection and jailbreak attempts
• Filtering sensitive or toxic model responses

Configure LLM inference and serving on NVIDIA GPUs with TensorRT-LLM. Covers quantization, which reduces numerical precision, batching requests during generation, and distributing models across GPUs to improve processing capacity and response times.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up an LLM server on NVIDIA GPUs
• Configuring FP8 or INT4 quantization
• Distributing large models across multiple GPUs

Configure LLM inference and serving on NVIDIA GPUs with TensorRT-LLM. Covers quantization, which reduces numerical precision, batching requests during generation, and distributing models across GPUs to improve processing capacity and response times.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up an LLM server on NVIDIA GPUs
• Configuring FP8 or INT4 quantization
• Distributing large models across multiple GPUs

Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures in LLM applications
• Comparing prompt versions and evaluation scores
• Monitoring LLM costs and response times

Build an llms.txt file, a navigation guide for AI models, from a repository's structure. Summarize the project and organize links to essential documentation, specifications, examples, and configuration resources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating an AI-readable guide to a repository
• Organizing links to documentation and specifications
• Prioritizing essential project information

Create or review llms.txt, a Markdown guide to a website for AI systems. Discover important pages, write factual summaries, check links and formatting, and produce a ready-to-save file or an improvement report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create llms.txt from a website's public pages
• Check an existing file's format and links
• Find important pages missing from the guide

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Build safety checks for LLM prompts and responses using Meta's LlamaGuard. This skill covers content classification, batch moderation, serving a moderation API, and connecting checks to NeMo Guardrails.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Screening user prompts before sending them to an LLM
• Checking generated responses before displaying them
• Building an API for content safety checks

Compress large language model weights to four bits with AWQ, a method that uses model activation patterns to limit quality loss. Follow workflows for quantization, loading compressed models, and serving them with vLLM using less GPU memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running large language models with limited GPU memory
• Quantizing a model with domain-specific text
• Serving an AWQ model with vLLM

Connect an application's LLM calls through the Caveman Cloud gateway to measure usage and cost without changing request content. The skill updates connection settings and verifies the integration with one small, real provider request.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Start measuring an LLM application's usage and cost
• Route existing LLM calls through the gateway
• Verify a connection with a real request

Find LLM workflows in a codebase and label them by the job they perform, so Caveman Cloud can separate spending by workflow. Present a labeling proposal for approval, apply focused code changes, and check an affected execution path.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Breaking down LLM spending by job
• Inventorying LLM use in APIs, scheduled jobs, and scripts
• Separating evaluation spending from other workflows

Audit and improve `/llms.txt`, a curated starting point for AI tools browsing documentation. Check its location, the usefulness and accessibility of linked pages, and its separation from existing crawl and indexing controls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Audit llms.txt on a public documentation site
• Select useful API references and getting-started guides
• Check documentation links for availability

Analyze LLM serving traces to identify GPU time usage, opportunities to run operations concurrently, and known patterns for combining operations. Produce three consistent tables from saved traces or live captures on supported servers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Identify GPU operations that dominate inference time
• Compare input processing and token generation
• Trace GPU operations back to Python code

Compress LLM weights using HQQ quantization, which stores weights with fewer bits without calibration datasets. Follow workflows for precision settings, model saving, inference integration, and LoRA fine-tuning of quantized models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compressing LLMs without calibration datasets
• Serving quantized models with vLLM or HuggingFace Transformers
• Comparing bit widths and inference backends

Deploy language models with vLLM through an OpenAI-compatible API or offline batch processing. Tune response speed, throughput, and GPU memory use with quantization, which reduces numerical precision, and execution across multiple GPUs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploy an OpenAI-compatible language model API
• Generate text from many prompts in a batch
• Serve quantized models with limited GPU memory

Compress large language models to 4-bit weights to reduce GPU memory use and accelerate inference. Covers loading compressed models, configuring quantization, and using Transformers or vLLM, with guidance on hardware requirements and backend choices.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running large models with limited GPU memory
• Compressing a model with domain-specific sample text
• Serving AWQ models with vLLM

Helps speed up LLM text generation by comparing methods that predict and verify multiple tokens at once. Covers draft model selection, Medusa prediction heads, Lookahead Decoding settings, and deployment examples for inference services.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reducing chatbot response latency
• Comparing methods for faster code generation
• Selecting a small draft model for a larger LLM

Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a container for an LLM deployment
• Select images for embeddings and rerankers
• Check regional image addresses and GPU compatibility

Plan security controls for LLM applications and AI agents, covering malicious instructions, data leakage, model tampering, and API access. Includes guidance and code examples for filtering outputs, enforcing permissions, isolating networks, and recording audit events.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing defenses against prompt injection in AI agents
• Filtering personal information from LLM responses
• Designing authentication and rate limits for LLM APIs

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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