Starting AIEmpezar con IA
Solve one task, then repeat itResuelve una tarea y después repítelaStart with one real task. Learn AI as you go.Empieza con una tarea real. Aprende IA sobre la marcha.
Choose the right workspace, mode, and prompt, get a useful result, and stop at the depth that helps.Elige el espacio, el modo y la instrucción adecuados, consigue un resultado útil y detente en el nivel que te sirva.
Coffee-first move: bring one real task with you. “Help me answer this customer email” will teach you more than “show me AI.”Primer paso, café en mano: trae una tarea real. «Ayúdame a responder este correo» te enseñará más que «muéstrame la IA».
Starting AIEmpezar con IA
Choose what you want to learn.Elige lo que quieres aprender.
Start with one useful task or go directly to advanced systems, agentic development, or cloud operations.Empieza con una tarea útil o ve directamente a sistemas avanzados, desarrollo agéntico u operaciones cloud.
Bring this sentence: I need AI to help me ___ using context from ___.Trae esta frase: Necesito que la IA me ayude a ___ usando contexto de ___.
Not sure? Starting AI is the simplest first step.¿No estás seguro? Empezar con IA es el primer paso más sencillo.
Four learning guidesCuatro guías de aprendizaje
Pick the destination closest to your goal.Elige el destino más cercano a tu objetivo.
The guides build on each other, but they are not gates. Start where the work makes sense.Las guías se apoyan entre sí, pero no son barreras. Empieza donde tenga sentido para tu trabajo.
Advanced AI systemsSistemas de IA avanzados
Design the boundaryDiseña el límiteAgentic engineeringIngeniería agéntica
Build a bounded loopConstruye un ciclo acotadoAgentic + cloudAgéntico + nube
Make it safe to operateHazlo seguro de operarOptional references: look up a tool in Tool Atlas, or open Govern & Protect when data, affected people, or connected actions raise the risk.Referencias opcionales: busca una herramienta en el Atlas, o abre Gobernar y proteger cuando los datos, las personas o las acciones conectadas eleven el riesgo.
New here? Open the four-step guided walkthrough¿Es tu primera vez? Abre el recorrido guiado de cuatro pasos
Set your detail level, follow the visual map once, then use shortcuts whenever you return.Elige tu nivel de detalle, sigue el mapa visual una vez y luego usa los atajos cuando regreses.
Set depth
Choose how much detail you want.
Start the map
Watch how the pieces connect.
Start with the visual walkthrough below. It explains why model, app, mode, prompt, and checking all matter.
Start the mapFollow the trail
Let each section hand you to the next.
After the slideshow, continue through limits, why AI helps, the model/app/ecosystem map, and then the picker.
Jump later
Use shortcuts after the first pass.
Starting AI
See how the pieces fit together
Click through the map first. Each step zooms into one part of the workflow, then the next buttons walk you through the sections in order.
Map move: do not memorize the whole page. Follow the route once, then use the picker for the task in front of you.
What to do
Use your AI limits wisely
importantStronger models and thinking modes often use more of your plan limits. Use them when the task needs extra reasoning, not for every quick draft.
Limit move: draft fast, then upgrade only if the answer affects money, trust, time, safety, or a decision someone else will act on.
Routine work
Drafts, summaries, simple rewrites, basic emails, quick lists, and low-risk brainstorming.
lower usageHard work
Planning, research synthesis, code architecture, long documents, important decisions, and sensitive question prep.
higher usageSimple rule
If a bad answer would barely matter, use fast mode. If a bad answer would waste time, money, trust, or safety, use stronger mode and verify.
Human-centered why
AI should give you breathing room
Use AI to organize confusing situations, break big tasks into smaller steps, prepare questions, compare options, and get unstuck.
Use AI to check your own blind spots.
A draft can make perfect sense inside your context bubble, then sound totally different to someone seeing it cold. Ask AI to read it like an outsider, then use another model to challenge facts, tone, missing context, and assumptions.
Product vocabularyVocabulario de productos
Company → model → app → tool → agent → ADEEmpresa → modelo → app → herramienta → agente → ADE
These layers are related, but they are not interchangeable. Learn this quick map here; the company guides, Tool Atlas, and Agentic Development route add detail only when you need it.Estas capas están relacionadas, pero no son intercambiables. Aprende aquí el mapa rápido; las guías de empresas, el Atlas y la ruta de Desarrollo agéntico añaden detalle solo cuando lo necesites.
01 · Company / providerEmpresa / proveedor
Who makes or hosts itQuién lo crea o aloja
OpenAI, Anthropic, Google, xAI, Microsoft, Nous Research. One company may offer many models and products.OpenAI, Anthropic, Google, xAI, Microsoft y Nous Research. Una empresa puede ofrecer muchos modelos y productos.
02 · ModelModelo
The reasoning engineEl motor de razonamiento
GPT, Claude, Gemini, Grok, Llama, Mistral, DeepSeek. A model produces output; the surrounding product decides what it can see and do.GPT, Claude, Gemini, Grok, Llama, Mistral y DeepSeek. El modelo produce la salida; el producto decide qué puede ver y hacer.
03 · App / productApp / producto
The room where you use itEl espacio donde lo usas
ChatGPT, Claude app, Gemini, Grok, Codex app, Cursor. The room adds files, projects, memory, interface, and account policy.ChatGPT, Claude app, Gemini, Grok, Codex app y Cursor. El espacio añade archivos, proyectos, memoria, interfaz y política de cuenta.
04 · Tool / connectorHerramienta / conector
A bounded way to read or actUna forma limitada de leer o actuar
Web search, files, GitHub, email, APIs, MCP servers, image generation, terminals. Tools carry permissions and need review.Búsqueda web, archivos, GitHub, correo, APIs, servidores MCP, generación de imágenes y terminales. Las herramientas llevan permisos y necesitan revisión.
05 · AgentAgente
A loop that chooses steps and toolsUn bucle que elige pasos y herramientas
An agent works toward a goal, observes results, and continues until a stopping rule. Hermes Agent is a persistent agent runtime.Un agente trabaja hacia una meta, observa resultados y continúa hasta una regla de detención. Hermes Agent es un runtime persistente.
06 · ADE
The workspace around agent workEl espacio que rodea el trabajo de agentes
An Agent Development Environment coordinates tasks, worktrees, terminals, previews, review, and delivery. Orca ADE is the deep example in this guide.Un Entorno de Desarrollo de Agentes coordina tareas, worktrees, terminales, vistas, revisión y entrega. Orca ADE es el ejemplo detallado de esta guía.
Interactive picker
Find good fits for your task
Start with what you need to do or where the useful context already lives. If an answer feels off, add clearer context, use a stronger mode, or compare with a second model.
Better answer move: add four details to any suggested prompt: who it is for, what context matters, what format you want, and what would make the answer unusable. One specific example usually beats a broad request.
Model families
What each model family is for
These cards are quick mental maps, not full product limits. Each family includes apps, modes, connected tools, agents, and developer paths; use the short label as a starting handle, then expand the card for what it actually does.
Model move: open the card that matches the job, not the brand. The question is “what role do I need right now?”
Check current model releasesConsultar versiones actuales de modelos
A product, a model, and an open-weight release are different things. For example, Meta’s Muse Wonder is available through its API and Muse Code; that does not make it a new Llama download. Check access and deployment rights before choosing.Un producto, un modelo y una publicación de pesos abiertos son distintos. Por ejemplo, Muse Wonder de Meta está disponible mediante su API y Muse Code; eso no lo convierte en una nueva descarga de Llama. Comprueba acceso y derechos de despliegue antes de elegir.
Deep dives
Three everyday AI ecosystems
Open a card to understand what people use each ecosystem for, why they pick it, and which app surface fits the work in front of them.
Claude ecosystem
Thoughtful writing, long-document work, Artifacts, desktop/tool connections, and Claude Code for developer workflows.
- Why people pick it: polished tone, careful reasoning, long context, and better critique of messy drafts.
- Everyday use: messages, documents, meeting notes, writing polish, plans, and decision framing.
- Workspace use: Artifacts for reusable content/tools; Desktop/connectors when Claude needs surrounding files or apps.
- Developer use: Claude Code for terminal, IDE, GitHub Actions, code review, and repo-aware implementation workflows.
What this ecosystem is: Claude is the chat surface for writing, reasoning, documents, and analysis. Claude Desktop adds local desktop extensions. Connectors can bring in cloud tools. Artifacts turn substantial outputs into editable/reusable content beside the conversation.
Why people pick it: it is often a comfortable choice when the work is sensitive to tone, nuance, or long context: difficult emails, policy drafts, meeting summaries, strategy notes, essays, scripts, and “read this carefully” reviews.
How it is used: paste rough notes, drafts, or files and ask Claude to preserve intent while improving clarity. For bigger outputs, ask for an Artifact so the result can be edited, reused, or shared outside the chat.
Best app surface: Claude web/mobile for ordinary chat and document work. Claude Desktop when the work should connect to local files or desktop apps. Claude Code when the work is a real software project, terminal task, GitHub issue, or PR review.
Try this: “Read this as someone with no background context. What could sound unclear, cold, defensive, too intense, or different than I intend? Then rewrite it in my voice.”
Connector habit: before connecting tools, decide whether the context is local/private, cloud/team-based, or safe to paste manually. The best surface is the one that can see the right context with the least friction.
Advanced angle: Claude Code and Claude Code GitHub Actions fit repo-aware implementation, automated code review, PR/issue workflows, and custom agent workflows that should follow project standards.
ChatGPT + Codex ecosystem
General-purpose AI, files, images, voice, data analysis, research, writing/code blocks, custom assistants, and Codex for coding agents.
- Why people pick it: broad coverage: ask, draft, analyze files, work with images, talk by voice, research, and build.
- Everyday use: planning, learning, writing, spreadsheet/data cleanup, image/screenshot help, and quick second opinions.
- Workspace use: writing/code blocks for co-writing, debugging, and structured editing; file upload and data analysis for documents, PDFs, CSVs, and spreadsheets.
- Developer use: Codex across app, CLI, IDE extension, web/cloud, and GitHub-style review workflows.
What this ecosystem is: ChatGPT is the general assistant surface for conversation, writing, learning, file work, images, voice, search, deep research, writing/code blocks, custom GPTs, and memory. Codex is the coding-agent side of the ecosystem.
Why people pick it: it is the easiest “one place to start” when you are not sure whether the task is writing, research, planning, images, data, coding, or a mix of several things.
How it is used: upload a file to summarize or extract information, ask about a screenshot, talk through a problem by voice, use data analysis for spreadsheets/CSVs, or use writing/code blocks when you want a cleaner draft or implementation snippet inside the chat.
Best app surface: ChatGPT web app for files, research, and longer sessions. Mobile app for voice, photos, screenshots, and quick capture. macOS/Windows app for working beside desktop apps. Codex app / CLI / IDE extension for repos, terminals, multi-file changes, and reviewable diffs.
Try this: “Give me a quick answer first. Then give me the careful versión, what you assumed, and what I should check before using it.”
Codex habit: use ChatGPT when the problem is still fuzzy; use Codex when the work should touch real project files, tests, branches, PRs, or terminal commands.
Advanced angle: Codex is built for supervising coding agents across CLI, IDE, app, web/cloud, and repo workflows, with reviewable diffs and permissioned command execution.
Gemini + Google ecosystem
Google apps, Search, Android, Gmail/Drive/Docs context, photos, YouTube, Workspace, Deep Research, and Gemini Notebook.
- Why people pick it: the useful context is already in Google: Gmail, Drive, Docs, Sheets, Slides, Calendar, Photos, YouTube, Maps, or Android.
- Everyday use: find details in email/files, summarize Google docs, plan from Calendar/Maps, ask about photos, or understand YouTube content.
- Workspace use: Gemini in Workspace for docs, sheets, slides, drive, mail, meetings; Gemini Notebook for source-grounded study/research packets.
- Developer use: AI Studio, Gemini API, Android Studio, Gemini Enterprise Agent Platform, and Google Cloud workflows.
What this ecosystem is: Gemini is Google’s AI assistant across the Gemini app, Android, Search, Workspace apps, and developer tools. Google renamed NotebookLM to Gemini Notebook in July 2026; it remains the source-grounded research workspace for a defined set of materials.
Why people pick it: it reduces copying context around when the work already lives in Google. That can mean Gmail threads, Drive files, Docs, Sheets, Slides, Calendar items, Photos, YouTube videos, Maps plans, or Android screenshots.
How it is used: ask Gemini to summarize email or Drive context, turn a photo or flyer into a calendar/event task, create or improve Workspace docs/slides/sheets, or use Gemini Notebook to turn sources into cited Q&A, briefings, study guides, audio/video overviews, mind maps, reports, data tables, quizzes, slide decks, and infographics.
Best app surface: Gemini app for everyday Google-connected help, Android/mobile for voice/photos/screenshots, Workspace surfaces when you are already inside Gmail/Docs/Sheets/Slides/Drive, Deep Research when you need a report, and Gemini Notebook when the source packet matters more than open-ended chat.
Try this: “Which Google surface has the useful context for this task: Gmail, Drive, Docs, Sheets, Slides, Calendar, Photos, YouTube, Maps, Search, or Gemini Notebook? Tell me where to start and what to ask.”
Google habit: decide whether you need personal/app context, Workspace context, web research, or source-grounded Gemini Notebook work. Those are different jobs.
Notebook update: Gemini Notebook can find and import web or Drive sources, run Fast or Deep Research, and sync notebooks with Gemini. A secure cloud computer can write and run code for deeper source-backed analysis on eligible plans, with access rolling out by account and plan.
Advanced angle: Gemini fits AI Studio, Gemini API, Gemini Enterprise Agent Platform, Android Studio, multimodal workflows, and Google Cloud/Workspace automation concepts.
Workspaces and maps
Packaged workflows and strength map
Workspace move: pick the workspace that already has the useful context. Copilot for Microsoft work, Gemini Notebook for source packets, Codex for project files.
Genspark
Genspark 6.0 combines SecondBrain memory, Super Agent, Build/Office/Content suites, and GenTeam collaboration. Use it when one workspace should turn connected context into slides, sheets, docs, designs, code, or shared agent workflows; review what it can access and verify finished outputs.
Perplexity
Perplexity combines cited web answers with Computer for multi-step research and building, the Comet browser, and an API. Use it to map current evidence, then open and verify the decisive sources before acting.
Microsoft Copilot
Microsoft’s AI family: free Copilot for web help, Copilot Chat Basic for secure work chat, and Microsoft 365 Copilot for deeper Office, Teams, Outlook, and company context.
Free/personal Copilot: use it for web-grounded questions, writing, brainstorming, image creation, Edge page help, and everyday learning. The visible modes can include Quick response, Think Deeper, Study and learn, Smart, and Search.
Copilot Chat Basic for work: use it for secure work chat with web grounding, file/image upload, Copilot Pages, standard model access, and the model selector. It can work with uploaded files or context open in supported Microsoft 365 apps.
Microsoft 365 Copilot: use it when the answer needs meetings, emails, chats, people, files, or app-specific editing inside Word, Excel, PowerPoint, Outlook, Teams, OneNote, OneDrive, or SharePoint.
Model picker: Auto is the everyday default. Quick response is for speed. Think deeper is for harder reasoning. Some tenants also offer Claude and current GPT-family choices in the selector.
Recurring agent work: eligible tenants can schedule prompts for agents. Long-running task status, inherited sensitivity labels, and AI-media watermark controls vary by platform, license, and admin policy.
Prompt pattern: “Using the Microsoft 365 context I can access or attach, summarize what matters, list assumptions, draft the output, and tell me what names, dates, files, numbers, or permissions I should verify.”
Watch for: check your label: Copilot Chat (Basic), M365 Copilot (Basic), or M365 Copilot (Premium). That label affects app access, work-data grounding, agents, and priority access.
Useful second check: paste the draft into another model and ask it to read with zero company context. Ask what sounds unclear, risky, overconfident, or missing.
Common strength map
Notes and knowledge workspaces
Turn scattered context into better AI answers
Notetaking is how you capture raw context. Knowledge workspaces are where that context becomes reusable: project briefs, meeting notes, research sources, decisions, and prompts you can bring back to a model later.
Notes move: after a useful AI answer, save the context and final decision. Next time, paste the note instead of re-explaining everything.
A practical workflow for notes + AI
The point is not to collect endless notes. It is to keep enough organized context that an AI can help without guessing.
Capture: save messy notes, links, screenshots, transcripts, files, and decisions before they disappear.
Organize: turn raw notes into headings, bullets, dates, owners, source links, and open questions.
Ask: give the model the note plus a specific job: summarize, draft, compare, plan, or critique.
Compare: use another model or source-backed tool when tone, facts, or decisions matter.
Reuse: keep the cleaned note as future context instead of restarting from scratch every time.
Intermediate habit: make one reusable project brief per important project: goal, audience, decisions, constraints, open questions, source links, and next actions.
Advanced habit: keep AI-ready context files in Markdown so they can move between Obsidian, local folders, Codex, GitHub issues, Claude, ChatGPT, Gemini, and other tools without lock-in.
Use AI to teach yourself
Turn AI into a coach, not just an answer machine
The best learning use of AI is active: explain, practice, get feedback, apply it to something real, then save what you learned in notes.
Learning move: make the model quiz you before it explains more. If you cannot use the idea, you have not learned it yet.
The learning loop
If you only ask “explain this,” it is easy to feel fluent without being able to use the idea. Ask the AI to make you practice.
Explain: ask for the idea at your level, with no jargon unless it is defined.
Example: ask for a real example from your work, hobby, class, or project.
Practice: make the AI quiz you, give drills, or ask you to solve a small scenario.
Feedback: have the AI grade your answer and show what you missed.
Apply: use the idea on a real task, then save the result in your notes.
Intermediate habit: ask for spaced review: “quiz me tomorrow on the parts I missed today,” then save the weak spots in a note.
Advanced habit: build a source-backed study packet in Gemini Notebook or Markdown, then use Claude/ChatGPT/Gemini for practice, critique, and application projects.
AI judgment and blind spots
Use AI without letting it become an echo chamber
AI is designed to be helpful, but helpful can become too agreeable. A strong workflow asks models to challenge your framing, not just polish it.
Challenge move: after the first good answer, ask “what would a smart person disagree with here?” That is where the useful thinking starts.
The challenge loop
When the answer matters, treat the first response as a draft. Make the model name assumptions, critique weak points, read it from another perspective, and then improve it.
Frame: state your goal, what you believe, and what outcome you want.
Challenge: ask what you may be missing, over-weighting, or assuming.
Compare: run the same issue through another model or perspective.
Verify: check facts, tone, numbers, policies, source material, and real-world constraints.
Decide: use the critique to improve the work, not to outsource judgment.
Intermediate habit: after a model agrees with you, ask the opposite: “make the strongest case that I am wrong or missing something.”
Advanced habit: use different models for different jobs: one to draft, one to critique, one to fact-check, and one to simplify the final decision.
Comparison
Useful comparison table
| Family | Common strength | Mode to try | Useful surfaces | Watch out for |
|---|
Prompt engineering without jargon
Prompt stack: task + context + constraints + format + freshness + check
Prompt move: do not chase perfect wording first. Add missing context, ask for a useful format, then ask the model what it still needs.
Prompt stack visual
Use the stack as a checklist when a prompt is vague: say what you want, add useful context, set limits, ask for the format, flag whether current info matters, then ask what should be checked.
Prompt repair tool
This builder uses prompt templates, not live AI. It does not save the text you type.
Prompt + freshness builder
Follow-up prompt library
Prompt technique cards
Take it with you
Download the prompt cheat sheet
Includes the mode map, context map, prompt stack, freshness prompt, accuracy habits, starter prompts, and second-opinion prompts.
Carry-it move: keep the cheat sheet nearby, then customize one prompt for your actual work instead of starting from a blank box.