'accuracy' 검색 결과

Review an Anthropic Cookbook notebook for clear explanations, code quality, technical accuracy, and practical usefulness. It combines automated checks, including exposed credential detection, with a scored report and specific recommendations tied to sections and lines.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review a Cookbook notebook before publication
• Check introductions and conclusions against learning objectives
• Scan for hardcoded API keys and credentials

Simulate peer review through five perspectives covering journal fit, methods, subject expertise, interdisciplinary implications, and counterarguments. Receive separate review reports, an editorial decision, and a revision roadmap, or check whether revisions address earlier comments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Simulate peer review before journal submission
• Check research methods and statistical validity
• Test core arguments against counterarguments

Draft and revise scientific manuscripts while linking claims to verified evidence. The skill organizes source records, checks consistency across text and results, tracks reporting requirements, and reviews references and disclosures, with human verification and final approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting manuscript sections from verified evidence
• Checking consistency across text, tables, and results
• Reviewing references and reporting checklist coverage

Plan and review fluid simulations with FluidSim, from solver selection and resource estimates to output analysis and restart checks. Examine numerical accuracy through grid and time-step comparisons, conservation checks, and explicit limits on computing resources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Selecting a solver and reviewing simulation parameters
• Estimating memory, disk space, and runtime needs
• Inspecting spectra and conservation budgets

Find academic papers, verify authors and publication details, and create BibTeX entries, a structured format for references. Use it to organize bibliographies, remove duplicates, and check missing fields or mismatches with a manuscript.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find papers across multiple academic databases
• Convert DOIs into BibTeX references
• Remove duplicate bibliography entries

Create reusable agent skills or improve existing ones through test prompts and user feedback. Compare output quality, execution time, and token usage against a baseline, then refine instructions and descriptions so the skill is invoked in appropriate situations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a recurring workflow into a reusable skill
• Improve an existing skill using test results
• Compare quality, runtime, and token usage across versions

Create consistent records for evaluating AI agents, including task success, invalid tool calls, token use, and actual costs. Produce CSV tables, database definitions, and JSON validation schemas to support reproducible comparisons and checks for performance declines.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording agent evaluation results in a shared format
• Comparing a candidate agent with a baseline
• Tracking actual cost per solved task

Turn recurring tasks into reusable agent skills, or improve skills you already have. Draft instructions, compare test outputs with and without a skill, review quality and resource use, and refine descriptions so skills trigger for appropriate requests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a recurring workflow into a reusable skill
• Improve an existing skill using test results
• Compare output quality, runtime, and token use

Read, extract text from, split, merge, fill, or create PDFs while preserving originals. Check page counts, extracted text, and file sizes, and flag accuracy limits when scanned pages require optical character recognition.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extract text from PDFs and check page coverage
• Split, merge, or rotate PDF pages
• Fill PDF forms or create new PDFs

Explains how PluginEval combines structural checks and experimental model assessments into quality scores. Helps interpret low scores, identify improvement areas, set marketplace thresholds, and explain what evaluation badges actually represent.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating why a skill received a low score
• Improving descriptions that define when a skill should run
• Setting score thresholds for marketplace listings

Design and refine prompts for AI applications using examples, reusable templates, and structured outputs such as JSON. Test variations for accuracy, consistency, response time, and token usage to diagnose unreliable answers and formatting failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refine prompts that produce inconsistent answers
• Build reusable prompt templates with variables
• Design JSON outputs that follow a defined schema

Check important factual claims in courses or supplied content against available materials and authoritative sources. Review numbers, definitions, and contradictions, then receive a concise report with evidence and suggested corrections, or have generated course pages checked before delivery.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking facts in a completed course
• Verifying important numbers and technical definitions
• Finding contradictions between course pages

Check spreadsheet formulas for errors, broken references, inconsistent calculations, and unit mismatches. Review a selected range, one sheet, or an entire financial model, then receive findings ranked by severity with suggested fixes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking formulas in a selected range
• Investigating a balance sheet that does not balance
• Reviewing a financial model before client delivery

Create and review CSV geofeeds, files that publish location information for public IP addresses. Check formatting, country and region codes, and privacy concerns, then review suggested improvements in an HTML report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a location feed for public IP addresses
• Check CSV formatting and location codes
• Review privacy concerns in published location data

Create and analyze AI model evaluation experiments in Arize using the ax CLI, a command-line tool. Collect real model outputs, export results, compare evaluation scores, and identify examples where a new model performs worse.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing models on the same evaluation dataset
• Finding examples that perform worse after a model change
• Exporting experiment results for further analysis

Add a model page to the SGLang Cookbook using shared templates. Organize verified launch settings and measured benchmarks, create configuration files and documentation, and update navigation, vendor cards, and page validation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add documentation for a newly supported model
• Organize verified launch recipes by hardware
• Publish measured speed and accuracy results

Compare exchange-traded funds by fees, tracking accuracy, liquidity, and market-price differences from asset value. Use the China-focused framework and Python examples to screen products, examine sector rotation, and study portfolio allocation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing ETFs that track the same index
• Estimating the long-term impact of fees
• Checking cross-border ETF premiums

Build real-time detection for ransomware encryption by combining changes in data randomness with file modification rates, renames, and process activity. Define behavioral scores and response thresholds, then check detection accuracy and monitoring overhead.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building ransomware monitoring for endpoints and file servers
• Tuning detection thresholds to reduce false positives
• Investigating processes suspected of encrypting files

Forecast sales, demand, or sensor readings with Google's pretrained TimesFM model, without custom training. Check hardware and dataset memory needs, include external factors such as prices or holidays, and obtain forecasts with uncertainty ranges.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Forecasting future sales or demand from CSV data
• Including prices and holidays in sales forecasts
• Forecasting multiple stores' time series in batches

Reduce the memory needed to run large language models with GPTQ quantization, which stores model weights at lower precision. Covers loading compressed models, quantizing your own models, choosing execution backends, and connecting to memory-efficient fine-tuning.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run a language model within limited GPU memory
• Load a pre-quantized model for text generation
• Quantize and save your own trained model

Design and refine prompts for language model applications. Choose useful examples, create reusable templates and system instructions, and compare prompt variations using accuracy, consistency, response time, and token usage to guide improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refine prompts that produce inconsistent answers
• Create reusable templates with variables and conditions
• Select relevant input-output examples for a task

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Reduce GPU memory use by loading language models in 8-bit or 4-bit formats with bitsandbytes and HuggingFace Transformers. Includes configuration examples, QLoRA fine-tuning of compressed models, and optimizers that reduce memory needed during training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Load larger models within limited GPU memory
• Compare 8-bit and 4-bit model settings
• Fine-tune a model with QLoRA

Fine-tune large language models with LoRA and QLoRA while limiting GPU memory use. Guides training settings and adapter management, using small sets of task-specific weights that can be saved, switched, or merged into a base model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tuning an LLM with limited GPU memory
• Choosing LoRA settings and training layers
• Switching task-specific adapters on one base model