'lua' 검색 결과

Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare agent quality before and after prompt changes
• Test tool selection and multi-step task execution
• Evaluate resistance to prompt injection

Design criteria for using an LLM to judge AI responses through scoring or comparisons between two answers. Includes checks for order and length bias, plus validation against human judgments to assess evaluation reliability.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an automated evaluation pipeline for AI responses
• Compare responses after changing a prompt or model
• Define shared scoring criteria for reviewers

Compare agent, prompt, or tool configuration changes using versioned test cases and explicit outcome checks. Record each attempt, distinguish agent failures from evaluation or infrastructure problems, and report regressions, permission violations, and uncertainty.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare agent behavior before and after prompt changes
• Reproduce an observed failure with preserved evidence
• Run regression tests after a bounded fix

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.

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

Turn graded execution traces and production logs into model training examples and preferred-versus-rejected response pairs. Select useful records, remove sensitive information, exclude evaluation examples, and preserve links to the original runs.

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• Convert high-scoring successful runs into training examples
• Use expert-corrected failures as training examples
• Pair passing and failing responses to the same task

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Design backtests that evaluate trading strategies against historical data. Account for trading costs, avoid using future information or excluding delisted securities, and compare strategies using separate development and evaluation periods.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a backtesting system for trading strategies
• Check for bias in historical performance estimates
• Compare strategies after accounting for trading costs

Prepare for one company's upcoming earnings announcement with a compact HTML report. It combines the latest earnings call, competitor comparisons, valuation measures, and recent news, with links to supporting sources and calculation details.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a company earnings preview
• Comparing valuation with listed competitors
• Reviewing management guidance and key risks

Update financial models after earnings, new company guidance, or changes in assumptions. Revise forecasts, recalculate valuation and price targets, and summarize significant changes with their reasons and implications for the investment view.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Updating a financial model after quarterly earnings
• Revising forecasts after changes in company guidance
• Reflecting interest rate and currency changes in valuation

Create a single PowerPoint slide summarizing recent funding activity in selected sectors or companies. Using S&P Global Capital IQ data, it highlights funding totals, company valuations, major investors, key trends, and links to individual deals.

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• Weekly funding recaps for selected sectors
• Tracking funding rounds at watched companies
• Briefing colleagues on major deals and valuation changes

Create stock research notes combining analyst forecasts, reported financials, price history, and economic conditions. Compare market expectations with business performance, present valuation measures, and summarize an investment case with positive scenarios, risks, and potential catalysts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing an initial research note on a stock
• Comparing analyst forecasts with reported results
• Reviewing financial trends and stock valuation

Evaluates currency trades that aim to earn interest rate differences. It combines spot and forward prices, expected volatility, and historical trends to compare potential returns against risk across holding periods and present trade recommendations.

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• Comparing currency pairs for interest rate carry
• Choosing a trade tenor using forward curves
• Comparing carry returns against expected volatility

Prepare an initial equity research report through five separate tasks covering company research, financial forecasts, valuation, charts, and report assembly. Outputs include research notes, an Excel financial model, chart files, and a detailed Word report.

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• Researching a company’s business, competitors, and risks
• Building financial forecasts with three scenarios
• Estimating a price target using multiple valuation methods

Create and manage Arize datasets for evaluation and experiments using the ax CLI. Import records from files, append examples, inspect dataset versions, and export a chosen version for offline analysis, with checks for field consistency and export completeness.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating evaluation datasets from files
• Adding examples to an existing test set
• Inspecting dataset metadata and versions

Analyze public blockchain records to assess crypto market conditions. Combine network activity, large-holder transfers, decentralized finance liquidity, and valuation measures into a report with indicator comparisons, a composite score, and an interpretation of market signals.

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• Compare blockchain activity with price trends
• Examine large-holder transfers and exchange flows
• Review funds held in decentralized finance and trading liquidity

Break down major industrial trends into materials, components, and equipment to find listed suppliers at supply bottlenecks. Compare scarcity, financial performance, and valuation, then organize candidates into a ranked research board with supporting evidence and risks.

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• Find overlooked suppliers benefiting from AI infrastructure growth
• Compare supply constraints in energy transition supply chains
• Check valuations of companies at critical supply bottlenecks

Evaluate threat intelligence platforms, which collect and organize cyber threat information. Define requirements, compare integrations and operating costs, plan practical trials, and use weighted scoring to select a platform and outline its rollout.

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• Preparing requirements for TIP procurement
• Comparing platforms before a TIP migration
• Checking whether an existing TIP meets team needs

Plan authorized voice phishing assessments to evaluate how employees verify callers and report suspicious calls. Organize approved scenarios, record assessment outcomes, and summarize disclosure, verification, and reporting rates to review security awareness training.

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• Planning an authorized voice phishing assessment
• Evaluating caller verification and security reporting
• Reviewing the effectiveness of security awareness training

Run performance improvements as a measured workflow in OMX and Codex goal mode. Define an evaluation command and success criteria, make small reversible changes, and retain evidence before declaring the work complete.

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• Reducing CLI startup time against a defined target
• Repeating improvements with benchmarks and regression tests
• Recording evaluation results and blockers

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

Retrieve locally stored Chinese A-share prices, financial statements, and valuation context through the project's shared interfaces. Look up technical indicators, inspect available data fields, and check data dates when results disagree.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a stock's prices and valuation context
• Retrieving financial data across reporting periods
• Looking up technical indicators and their data dates

Evaluate vendor proposals, renewals, or competing offers by comparing costs, risks, performance, and business fit. Produce a structured review with total ownership costs, a recommendation, and concrete points for contract negotiations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluate a new vendor proposal
• Decide whether to renew or replace a vendor
• Compare two vendors side by side

Evaluate a proposed software change before implementation. It examines project files and official sources, compares options and trade-offs, and builds a concrete plan covering affected systems, effort, risks, and unresolved questions for the user's approval.

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• Discuss a major change before implementation
• Compare migration or configuration options
• Estimate affected files and implementation effort

Supports preparation for EU medical device regulation reviews by organizing device classification, technical documentation, clinical evidence, and post-market monitoring. Includes a tool to identify documentation and compliance gaps and prioritize the next steps.

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• Documenting a device classification rationale under MDR
• Checking technical documentation before a review
• Planning clinical evaluation and identifying evidence gaps