Cómo se hizo "De Chatbot a Compañero de Trabajo": Cómo usé la IA para escribir un post sobre IA

Estaba experimentando con casos de uso de IA y pensé: ¿por qué no usar Gemini Deep Research para escribir un post sobre el stack de ingeniería de IA para ingenieros junior/mid?

Este es el “making-of” de ese experimento, donde explicaré todos los pasos y compartiré los prompts que usé para crear el post. (Los compartiré con las erratas y errores de sintaxis también, para ser transparente sobre el proceso).

Primer paso: Preguntar a Gemini

Le pedí a Gemini que escribiera el prompt que debería usar con la función Gemini Deep Research.

👨 Yo:

I want to create a full AI generated post for my blog.
The topic is an explanation (not too much thecnical, for a junior or mid software developer) about the different layers in software development with AI: LLM, agent, CLI or IDE, tools, mcps, skill.md, agents.md, orchestrators like https://www.vibekanban.com/, etc. The goal is allow the audience to understand what is the use case or what is each tool used for.
The outcome should be a markdown file, an user could read in a max of 12m, 
I need a prompt for gemini Deep Research to investigate and create this post

a secondary goal is to create another post explaining the steps I or you followed  to create the post, from this prompt i am writing , passing by the deep research prompt you will generate and the steps it followed to gather the information necessary to write the post.

Before start, ask me anything can improve the outcome

El LLM resumió la petición y me hizo algunas preguntas, tal como solicité en la última línea del prompt anterior.

🤖 Gemini:

This is a fantastic topic. The AI-for-developers landscape is evolving so fast that terms like "MCP," "Agent," and "Orchestrator" are often used interchangeably, confusing many junior and mid-level developers. A clear, layered explanation will be very high-value.

To make sure the Deep Research prompt I generate for you hits exactly the right notes (and to ensure the secondary "meta-post" is accurate), I have a few clarifying questions:

1. The "Layers" Hierarchy
   You mentioned several concepts (LLM, Agent, CLI/IDE, Tools, MCPs, skill.md, Orchestrator). To structure the research correctly, how do you visualize the relationship?

Option A (The Stack): LLM is the bottom (Foundation), Agents sit on top, Tools are what Agents use, and the IDE is the interface where the human sits.

Option B (The Workflow): Grouped by phase of work (e.g., "Planning" vs. "Coding" vs. "Testing").

My assumption: Option A is usually clearer for this audience. Is that your preference?

2. Specific Technologies & Examples
   You mentioned vibekanban.com (which appears to be a newer or niche visual orchestrator), skill.md, and agents.md.

Context: Are skill.md and agents.md references to specific standards (like the ones used in Cline/Roo Code or similar agentic tools), or are you referring to general documentation practices?

Standard Tools: Should the research explicitly include widely known tools to anchor the reader? (e.g., Anthropic's MCP, Cursor, Windsurf, LangChain, or Cline)?

VibeKanban: Is this post specifically to highlight VibeKanban (e.g., a spotlight or sponsored context), or is it just one example among many orchestrators?

3. The Tone and "Takeaway"
   Tone: "Not too technical" can mean "explain it like I'm 5" or "explain it like I'm a smart developer who just hasn't used AI agents yet." I assume the latter?

Actionable Advice: Do you want the post to just explain the terms, or do you want to recommend a specific "Getting Started" stack? (e.g., "Start with Cursor, then try building an Agent").

4. The Meta-Post (Secondary Goal)
   For the second post about how we built this:

Do you want the "Deep Research" prompt to explicitly include instructions to log its own process for this purpose? (i.e., "As you research, keep a diary of what search terms failed and which succeeded").

Once you answer these, I will craft a comprehensive Deep Research Prompt that you can copy-paste, designed to yield a perfectly structured blog post.

Le di el contexto adicional:

👨 Yo:

1. Both but not mixed
2. Skill is https://agentskills.io/home and agent.md is https://agents.md/. research widely known tools. Not about vibekanvan specifically, about the workspaces and agent orchestrator
3. the audicence are junior / mid who know a little bit about AI and used copilot or similar tools
4. yes

I forgot to mention i want you to explain the modes: ask, agent, plan, debug, etc

Me devolvió una explicación y el prompt final para usar con Gemini Deep Research en markdown.

🤖 Gemini:

This is a perfect structure. By distinguishing between the Stack (the technical layers) and the Workflow (the functional modes), we can give junior/mid developers a mental model that actually makes sense of the chaos.

Here is the Deep Research Prompt optimized for Gemini. You can copy and paste this directly.

1. The Deep Research Prompt
   Copy and paste this into Gemini:

🤖 Gemini:

I need you to research and write a comprehensive, educational blog post (approx. 12-minute read) targeting junior-to-mid-level software developers. The goal is to demystify the "AI Engineering Stack" by breaking it down into clear layers and explaining the specific purpose of each tool or standard.

**Critical Instruction for Meta-Analysis:**
As you perform this research, strictly log your process. I need a record of:
1. The specific search queries you used.
2. Which concepts were initially ambiguous and how you clarified them.
3. How you structured the information (e.g., did you change the outline based on findings?).
*I will ask for this "Search Journey" log after the blog post is written.*

### The Blog Post Requirements:

**Title Idea:** "From Chatbot to Coworker: Understanding the AI Dev Layers" (or similar)

**Tone:**
* Clear, "senior engineer mentor" vibe.
* Not too academic, but technically accurate.
* Avoid hype; focus on utility.

**Structure & Key Concepts to Cover:**

**Part 1: The Stack (The "What")**
Explain the hierarchy using a "Brain -> Hands -> Worker -> Manager" analogy or similar:
1.  **The Foundation (LLM):** Briefly explain this is just the raw intelligence (Claude 3.7, Gemini 1.5, GPT-4). It has no memory or hands yet.
2.  **The Context & Capabilities (Standards & Protocols):**
    * **MCP (Model Context Protocol):** Explain this as the "USB-C for AI"—a standard way for AIs to connect to data sources (Postgres, Slack, GitHub) without custom code.
    * **Context Files (`agents.md`):** Explain this as "README for Robots." It tells the AI *about* the project (rules, stack, conventions) so it doesn't hallucinate. (Source: agents.md).
    * **Skill Definitions (`skill.md`):** Explain this as "Training Modules." It tells the AI *how* to perform specific tasks (e.g., "How to run a migration"). (Source: agentskills.io).
    * *Key distinction:* `agents.md` is project context; `skill.md` is reusable capability.
3.  **The Worker (The Agent):**
    * Explain the "Agentic Loop": It doesn't just talk; it **Plans**, uses **Tools** (CLI, File edit), and **Debugs** its own mistakes.
    * Mention tools like **Cline** (formerly Roo Code) or **Windsurf** as examples of agents that live in your editor.
4.  **The Workspace (The Orchestrator):**
    * Explain that as agents get better, we stop just "chatting" and start "managing" them.
    * **Orchestrators:** Tools like **VibeKanban** (visualize agents working in parallel on a board) or **OpenDevin/AllHands**.
    * **The concept:** You assign a ticket to an agent, it creates a git worktree, tries to fix it, and requests review. You are the manager; the AI is the junior dev.

**Part 2: The Modes (The "How")**
Explain the different ways a dev interacts with these layers (referencing tools like Cursor/Windsurf/Cline):
1.  **Ask/Chat Mode:** "What does this function do?" (Context lookup).
2.  **Plan/Architect Mode:** "Read all these files and propose a refactor strategy." (Reasoning without editing).
3.  **Agent/Edit Mode:** "Implement the plan, run the tests, and fix errors until green." (Autonomous loop).

**Part 3: The Takeaway**
* How a junior dev should start: Don't jump straight to orchestrating 10 agents. Start with `Ask`, then master `Agent` mode in IDE, then look at `Orchestrators`.

**Research Verification:**
* Verify the current state/definition of `agents.md` vs `skill.md` to ensure the distinction is accurate.
* Verify `VibeKanban`'s specific workflow (git worktree isolation) as a prime example of orchestration.

🤖 Gemini:

2. Explanation of the Prompt Strategy
   Here is why I designed the prompt this way, so you know what to expect:

The "Context vs. Capability" Distinction: I explicitly asked the model to research the difference between agents.md (Project Context) and skill.md (Reusable Skills). This is often confused, and clarifying it will make your post authoritative.

The "USB-C" Analogy for MCP: I included this specific analogy instruction because MCP can be abstract. Framing it as a standard connector helps the audience grasp it immediately.

VibeKanban as a Concept, not just a Tool: I framed VibeKanban specifically around its "Git Worktree" feature. This is the "technical nugget" that makes the post valuable to developers—it explains how parallel agents actually work without breaking your code.

The "Search Journey" Log: I added the Critical Instruction at the very top. This ensures Gemini "remembers" to track its own thinking process, which we need for your second blog post.

Next Step:
Once you run this prompt and get the blog post, paste the result (or the "Search Journey" section) back here. I will then help you format the Secondary Post about "How we wrote this using AI."

Segundo paso: Preguntar a Gemini Deep Research

Con el prompt, abrí una nueva conversación de Gemini con la función Deep Research y lo pegué allí. Esperé el resultado.

Después de unos minutos de trabajo, devolvió el informe y simplemente pedí exportarlo en formato markdown para poder usarlo en el blog post.

Solo como referencia, utilizó 24 fuentes para generar el informe.

Tercer paso: Refinar el resultado

El resultado era bueno, pero demasiado largo para un post, y algunas referencias estaban desactualizadas (mencionaba Gemini 1.5, pero estamos en Gemini 3 Pro), así que volví a la conversación inicial y pedí que lo resumiera y actualizara las referencias.

Nota: no pegué el informe, solo lo mencioné y lo obtuvo de la otra conversación.

👨 Yo:

the report generated is too long and seems partially outdated, talkss about gemini1.5 and now we have gemini 3, same with claude and openai model, i can no trust in a report not updated

Y finalmente me dio esta respuesta:

🤖 Gemini:

Here is the comprehensive, ready-to-publish blog post. It is structured to be clear, actionable, and uses the latest terminology for 2026.

Following that, I have included the "Meta-Post" detailing exactly how we built this.

(y el contenido del post, pero no lo reproduciré aquí ya que lo tienes en el post final)

Cuarto paso: Generar la imagen de portada

Y como último paso, le pedí que generara una imagen de portada:

👨 Yo:

ok, i need to generate the cover for the posts, write a prompt based in previous request, and generate it

Y eso es todo, ya tengo el post y la imagen de portada listos para ser publicados.

Conclusión

Aunque no quiera leer posts generados por IA, quería experimentar con el proceso de crear uno. Sé que este proceso puede automatizarse completamente usando agentes en un bucle de generación, correcciones, mejoras de SEO, generación de imágenes, etc., pero quería mantenerlo simple.

El resultado es un post legible, algo que probablemente yo nunca escribiría de la misma manera, y eso es lo que marca la diferencia. Creo que el valor de la creación de contenido en general tiende a 0, y los resultados son buenos, pero esto hace más valioso el contenido “hecho a mano”, ya que tiene detrás la experiencia de vida del escritor, el toque personal, sus opiniones.