'evaluation' 검색 결과

Build and check clinical machine learning workflows with PyHealth, from loading electronic health records, signals, or images to training and evaluation. Includes patient-level data splits, prediction task checks, model checkpoints, and medical code mapping.

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• Prototype mortality and readmission prediction models
• Check patient splits and data leakage
• Train and evaluate sleep staging models

Build Python AI applications on Microsoft Foundry with azure-ai-projects. Provides examples for creating agents, connecting tools, managing conversations and project resources, and running evaluations through Foundry and OpenAI-compatible clients.

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• Build an agent that searches uploaded documents
• Implement conversation threads and response handling
• List model deployments and project connections

Deploy and operate AI agents with AWS Bedrock AgentCore. Choose services for hosting agents, connecting external APIs, managing credentials and conversation state, monitoring activity, and assessing agent quality through automated evaluations.

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• Plan an agent deployment on AgentCore
• Expose a REST API as an agent tool through MCP
• Configure credentials and access controls

Plan and build AI agents through selected stages of design, implementation, tool integration, memory, and evaluation. The workflow points to specialized skills for single agents, coordinated agent teams, and stateful workflows using CrewAI or LangGraph.

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• Designing an autonomous AI agent
• Building a team of agents with distinct roles
• Creating stateful workflows with LangGraph

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.

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

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.

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

Configure supervised fine-tuning for models that process images and text. Define which components remain unchanged, set LoRA adapters for updating selected model weights, choose image resolution limits, and investigate training runs whose evaluation results fail to improve.

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• Configure training for an image-and-text task
• Decide whether to train vision layers
• Investigate training with flat evaluation scores

Investigate LLM application failures with Phoenix CLI by reviewing execution records, recording observations, and grouping mistakes into counted categories. Inspect datasets and experiments to identify which failure types deserve evaluation tests first.

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• Investigating failures in LLM applications
• Grouping observed mistakes into failure categories
• Choosing which evaluation tests to build first

Build and run evaluations for AI applications using Phoenix. Start with observed failures, create checks in code or use a language model as a judge, run experiments, and validate automated judgments against human labels.

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• Turning observed failures into evaluation criteria
• Building code-based or language-model evaluators
• Evaluating retrieval and answer faithfulness in RAG

Improve LLM prompts using Arize production traces, evaluation results, and human feedback. Extract current instructions, identify recurring failures, revise the prompt, and compare experiments to measure changes in output quality and catch regressions.

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• Improve a prompt using failed production traces
• Fix responses that use the wrong output format
• Review unsupported answers in retrieval-based responses

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

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

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• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

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

Design tests that assess an AI agent's capabilities and reliability. Covers repeated runs to measure variation, checks for required behavior, and adversarial testing to expose failures that ordinary successful examples may miss.

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• Measuring variation across repeated runs of the same test
• Checking whether an agent follows required behavior
• Testing responses to unexpected or adversarial inputs

Learn how to author a third-party gbrain skillpack through a reference example. It explains required files, ten validation criteria, tests, and evaluation coverage, helping authors understand what to check before packaging and publishing.

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• Learning the structure of a gbrain skillpack
• Checking requirements before publication
• Understanding test and evaluation files

Create evaluation drafts for existing skills using actual conversations and user corrections. Produce criteria and repeatable test cases, distinguish evidence from specification assumptions, and prepare an anonymized draft for human review.

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• Turn user corrections into repeatable test cases
• Compare promised behavior with actual usage
• Draft an evaluation from conversation history

Build and maintain a medical device quality management system using ISO 13485:2016 workflows. It supports gap analysis, controlled documentation, internal audits, corrective and preventive actions, process validation, and supplier evaluation, with templates and an audit checklist generator.

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• Planning a medical device quality management system
• Standardizing document approval and revision control
• Preparing internal audits and certification reviews

Prepare a graded Claude Managed Agent for recurring schedules, event triggers, or on-demand use. Validate timing, build deployment request data, optionally include self-evaluation for each run, and document the next steps in a roadmap.

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• Schedule an agent to run every morning or weekly
• Prepare a curl trigger for an external event
• Add self-evaluation to each recurring run

Plan a Claude Managed Agent around one clear job, then route work through setup, launch preparation, evaluation, and scheduling. Produce execution plans and curl commands that users run with their own Anthropic API key.

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• Defining one clear job for a managed agent
• Preparing launch commands for an Anthropic account
• Planning evaluation and revision with an iteration limit

Improve code, prompts, skills, or agent definitions through repeated changes and evaluation. Measure numerical targets such as latency or size, or use an LLM to judge pass-or-fail criteria, keeping changes that improve the result.

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• Improving page load performance
• Reducing build output size
• Refining prompts against evaluation criteria

Create and improve prompts for agents, workflows, and AI evaluations. Reusable Handlebars templates, which insert data into a fixed structure, help generate consistent instructions from YAML or JSON inputs for use in other tasks.

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• Improving prompts for recurring tasks
• Generating agent briefs from templates
• Creating AI evaluation and scoring prompts

Design systems that use language models to judge AI responses. Covers scoring criteria, comparisons between two answers, bias reduction, confidence calibration, and checks against human judgments to make automated quality assessments more consistent and traceable.

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• Compare responses from different models
• Run A/B tests for prompt changes
• Create shared scoring rubrics

Design loops that improve an agent’s own prompts, workflows, or execution code from failure records. Define bounded edits, preserve candidate histories, and accept changes through independent tests that check improvements and regressions.

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• Designing agent self-improvement from failure records
• Defining acceptance checks for self-modifying workflows
• Diagnosing score gaming and stalled improvement

Design the controls around autonomous agents: what they can edit, how results are evaluated, how progress is logged, and when human approval is required. Build research and experiment loops with clear recovery and review rules.

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• Designing autonomous research and experiment loops
• Separating editable work from fixed evaluation rules
• Preserving progress and failed attempts across long runs