Results for “ai-engineering”

AI Engineering Toolkit workflow skill. Use this skill when the user needs 6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Find relevant lessons in AI Engineering from Scratch from a topic, question, or bug. Get a short selection of lessons, necessary prerequisites, and the next learning command, with dedicated routes for specialized subjects.

• Finding lessons on a topic you want to learn
• Locating lessons that explain a bug's underlying cause
• Choosing what to study next based on your progress

Start AI Engineering from Scratch with a brief interview and placement quiz. Create a persistent LEARNING.md study plan recording your goals, starting phase, pace, and learning path for later sessions with the learn skill.

• Creating a study plan when starting the curriculum
• Choosing a starting phase based on current knowledge
• Recording learning goals, available time, and a build goal

Take a ten-question interactive quiz across five AI and machine learning areas. Use the results to find a starting point in a 523-lesson curriculum, identify topics to review, and estimate the time for your learning path.

• Find where to begin studying AI and machine learning
• Skip familiar material in the curriculum
• Identify areas that need review

Study AI engineering through interactive terminal lessons with explanations, coding exercises, and quizzes. The tutor follows your recorded progress, revisits previous material, adapts to your answers, and logs results and topics that need review.

• Resuming lessons from recorded progress
• Learning concepts through coding exercises
• Checking understanding with lesson quizzes

Learn AI engineering by building a project one stage at a time. The tutor guides predictions, learner-written code, automated checks, and reflection, then records progress so the next session can continue from the right stage.

• Learn AI engineering through hands-on projects
• Resume a project from the next unfinished stage
• Find coding mistakes with step-by-step hints

Plan AI application work across six areas: prompt evaluation, context allocation, retrieval-based answer generation, agent security, evaluation pipelines, and product strategy. This overview explains the approaches; the individual workflow packages, test cases, and CI templates are not included.

• Planning prompt evaluation before deployment
• Allocating context capacity and designing retrieval-based answers
• Planning an authorized AI agent security assessment

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

The English summary is being prepared. Showing the original description.

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

The English summary is being prepared. Showing the original description.

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

The English summary is being prepared. Showing the original description.

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

The English summary is being prepared. Showing the original description.

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

The English summary is being prepared. Showing the original description.

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

The English summary is being prepared. Showing the original description.

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

The English summary is being prepared. Showing the original description.

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

The English summary is being prepared. Showing the original description.

Learn to examine compiled programs through one interactive lesson per session. Labs and quizzes cover file formats, machine instructions, program behavior, and analysis tools, while recorded observations provide evidence for completing each lesson and resuming later.

• Learn to read programs you compiled yourself
• Study binary formats and machine instructions
• Check analysis claims against observed output

Create animated lesson diagrams, sliders that update figures, and interactive panels that explain how systems work. It also builds moving dot patterns for page headers, with rules for readability, themes, and reduced motion.

• Animate a process in a lesson
• Explore a formula with a slider
• Show a system's behavior through one control

Build an interactive page that explains a process as a machine viewed from an angle. A tested simulation keeps movement, numbers, and explanations consistent, while controls let readers step through operations and explore failures.

• Explain a cache or queue through an interactive machine
• Show what fails when one process step breaks
• Create a standalone visual explanation page

Create static SVG diagrams, scalable images for lessons, READMEs, and manuals. It uses real system names, nine diagram types, and consistent colors and arrows, with checks for readable labels, accessible descriptions, and valid markup.

• Add a diagram of system interactions to a README
• Explain state transitions in a manual
• Illustrate data flow from logs or specifications

Apply shared visual and writing rules to course pages, figures, and manuals. Check colors, fonts, labels, layouts, plain technical English, dark themes, keyboard focus, touch targets, and reduced-motion behavior against defined reference values.

• Applying consistent colors and fonts to course pages
• Checking dark theme colors and text readability
• Standardizing figure labels and card layouts

Create a substantial technical manual for one exact system version. Organize authoritative sources, record offline inputs and outputs, explain behavior with figures, and audit the text and supporting evidence for consistency.

• Documenting a protocol or file format at a fixed version
• Building reproducible offline request and response records
• Adding a section with figures and traceable sources

Run a local check of test evidence and retain source paths in the result. The workflow stops when required evidence is missing, keeping the findings tied to their sources.

• Checking local test evidence
• Keeping source paths in check results
• Stopping a check when evidence is missing

Learn to create, discover, invoke, secure, evaluate, package, and port Agent Skills through one interactive lesson at a time. Practical checkpoints and quizzes track demonstrated progress in a local file, so later sessions resume where you stopped.

• Learn to build and invoke a minimal Agent Skill
• Understand discovery and automatic routing
• Check permission, sandbox, and trust boundaries

Review database migration files before merging changes. It gathers evidence about what the changes do, rollback limits, database locks, and risks from large data volumes, then reports blockers when evidence needed for a decision is missing.

• Review database changes before merging
• Check rollback limits
• Assess database locks and data-volume risks