DEV Community: Carlos Polanco

The Six Faces of OpenAI Models Roles

Carlos Polanco — Wed, 12 Mar 2025 09:50:00 +0000

That was the moment I realized most people — developers included — only knew half the story. Maybe you’ve heard of user, assistant, and system. But what if I told you there were more? Roles that quietly power the way AI conversations unfold, ones that could make or break how your application interacts with OpenAI’s models.

And here’s the kicker: some of them are hiding in plain sight.

Let’s break this down, make it crystal clear, and give you the edge in mastering these six roles.

Act 1 — The Familiar Faces
Think of an AI conversation like a play. Now, every play needs a few key characters, right?

User — That’s you, the star of the show, asking questions, making requests, and initiating conversations. (“Write me a haiku about programming.”)
Assistant — The assistant is the AI itself, responding to your prompts and carrying the conversation forward. (“Ah, a haiku? Of course! ‘Bugs in tangled code / Whisper secrets line by line / Debugging brings peace.’”)
Developer (formerly system) — This one often confuses people. It’s the unseen director, setting the stage with crucial behind-the-scenes instructions that shape the AI’s personality and behavior. (“You are a helpful but sassy coding assistant.”)
At this point, you might be thinking, That’s it, right? That’s all I need to know?

Not so fast.

Because beyond these roles, there’s a hidden tech layer most don’t talk about.

Act 2 — The Unsung Heroes
Here’s where things get interesting. Let’s introduce three lesser-known, yet incredibly powerful, roles that make AI responses more dynamic and functional.

System (or Developer, depending on the version) — Think of this as a director’s note given before the play begins. It sets general instructions that shape how the AI should behave within an app. While in newer versions, “developer” replaces “system,” they serve the same purpose. (“Only respond with JSON-formatted answers.”)
Tool — This is where AI conversations really level up. Imagine asking, “What’s the weather like in New York?” The assistant doesn’t just take a guess — it calls an external tool (like a weather API). The response from the tool is then passed into the conversation. (“{“location”: “New York”, “temperature”: “72°F”}”)
Function — This one often gets mixed up with “tool,” but here’s the difference: Where tools send results, functions send requests. The AI essentially writes a function call and hands it off. The function itself fetches the data. (“{“name”: “get_weather”, “arguments”: {“city”: “New York”}}”)
Confusing? Let’s untangle it. Think of it this way:

Tool: “Hey, I asked a weather API, and here’s what it gave us.”
Function: “I need the weather — go call the ‘get_weather’ function and fetch it for me.”
Facilities like function calling make AI do more than just guess. It’s how assistants connect to databases, run calculations, or even book appointments.

Act 3 — Why This Matters
Here’s where most developers miss out: Understanding these roles gives you direct control over AI behavior.

Want the AI to have a distinct personality? That’s your developer/system role.
Need responses from external data sources? You’ll be using tools and functions.
Struggling to differentiate system vs. tool vs. function? Just remember:
System/Developer is the rule-setter,
Tool delivers external responses,
Function makes a function call request.
Now, imagine what you could build if you fully leveraged all six roles.

Chatbots that tap into live stock prices. AI assistants that calculate travel costs in real-time. Automated agents that navigate workflows with nuanced instructions.

The possibilities? Endless.

Want to Go Deeper?
If you’re ready to put these roles into action, try them out in OpenAI’s API. Play around with function calling, tool inputs, and developer prompts. You’ll see the AI transform from a static Q&A bot into a dynamic, task-oriented powerhouse.

Got questions? Drop them in the comments or tweet me at @theaidevelover. Let’s make AI work for you. 🚀

Why Does AI Keep "Forgetting"?

Carlos Polanco — Thu, 27 Feb 2025 19:37:53 +0000

I remember the first time I tested an AI model for a long, detailed session. I had carefully set up my instructions, refined my prompts, and finally started getting the responses I wanted.

Then, out of nowhere…

It started acting like we had never spoken before.

Wait. What? Was all that effort wasted? Did the AI just erase everything we built so far?

If you've ever felt this frustration when working with AI models, I get it.

But here's the truth:

AI doesn't actually forget — the way it processes conversations just makes it seem that way.

And once you understand what's happening under the hood, you'll know exactly how to work around it.

The Reason AI "Forgets" (It's Not What You Think)

Imagine you're having an intense brainstorming session, throwing out ideas left and right. At some point, you can't keep everything in your head, so you hold onto only the key points that matter most.

That's exactly how AI models work.

They don't have permanent memory — they rely on something called tokens to manage conversation length.

How Token Limits Affect AI Memory

Here's how it plays out:

Each response operates within a limit of how much text the AI can process at one time.
When the conversation gets too long, older details are trimmed out to make space for new ones.
The model doesn't "remember" the way humans do — it prioritizes what's most relevant for continuity.

That same process can make AI inconsistent — especially when you're working on:

✅ Business strategies

✅ Fine-tuning marketing prompts

✅ Developing apps that require precise control

Making AI Work for You, Not Against You

What if I told you there's a way to bypass this limitation?

While commercial models struggle with long-term context, you can train AI to retain crucial details using techniques like:

✅ Fine-tuning certain patterns so the model better retains past interactions.

✅ Distillation to streamline learning and capture essential data.

✅ Vector searches that allow AI to pull past insights when needed — even if they were "forgotten" in the active conversation.

And that's exactly why I built FlowAI.

With FlowAI, you can design prompt flows that preserve context over longer interactions. Instead of constantly resetting, this system helps maintain continuity so your AI-generated responses stay:

✔ Consistent

✔ Coherent

✔ Aligned with your goals

No more repeating instructions.

No more trying to "remind" the model of past steps.

Just clear, structured AI conversations that actually build on what you've already done.

Want to See FlowAI in Action?

🎥 Watch the demo here

¿Cómo puede la IA encontrar mi SITIO WEB?

Carlos Polanco — Thu, 20 Feb 2025 17:08:31 +0000

¿Te has dado cuenta de que cada vez navegamos menos en la web tradicional? En lugar de abrir tu navegador y escribir en Google, simplemente le preguntas a tu asistente de IA:

“¿Qué es el SEO y cómo puede ayudar a mi sitio web?”

En segundos, recibes una respuesta clara y concisa, sin necesidad de desplazarte por múltiples páginas.

Ahí fue cuando surgió mi preocupación. Como desarrollador, siempre he confiado en estrategias tradicionales de SEO para aumentar la visibilidad de mi sitio. Pero con la llegada de la IA, que puede buscar y comprender el contenido de manera semántica, comencé a preguntarme:

¿Cómo buscan las IAs en la web y cómo puedo asegurarme de que mi sitio esté optimizado para ellas?

El Desafío del Doble Entrenamiento

Al principio, pensé que mejorar mi SEO tradicional sería suficiente. Optimizaba palabras clave, mejoraba la velocidad de carga y aseguraba una estructura de enlaces limpia.

Un pequeño avance, pero pronto me di cuenta de que no era suficiente. Las IAs no solo buscan palabras clave; buscan contexto, relevancia y profundidad. No se trataba solo de hablar el idioma de Google, sino de hablar el idioma de la IA.

Esto significaba crear contenido más rico, implementar datos estructurados y garantizar que cada página ofreciera valor real y relevante.

La Revelación: Se Necesita una Nueva Estrategia

Consultar con diferentes modelos de IA reveló una verdad fundamental:

1. Contenido de Calidad y Relevante

Asegúrate de que tu contenido responda a las preguntas de los usuarios. Piensa en cómo las personas formulan sus consultas y usa lenguaje natural que lo refleje.

✅ En lugar de simplemente escribir sobre “SEO”, crea una guía completa que responda preguntas específicas, como:

¿Cómo funciona el SEO en 2025?

2. Datos Estructurados

Implementa schema markup usando JSON-LD para ayudar a las IAs a entender el contexto de tu contenido.

🛠️ Esto mejora la elegibilidad para fragmentos enriquecidos y resultados de búsqueda por voz.

3. Actualización y Autoridad del Contenido

Las IAs priorizan contenido actualizado y confiable.

🔄 Actualiza regularmente tu contenido y construye backlinks desde sitios de confianza para señalar autoridad.

4. Interacción del Usuario

Las IAs rastrean la interacción de los usuarios. Métricas como el tiempo de permanencia y la tasa de rebote les indican si tu contenido es valioso.

📊 Fomenta la interacción haciendo que tu contenido sea atractivo, bien estructurado y fácil de navegar.

Un Ejemplo Práctico

Supongamos que tienes un sitio sobre energía solar:

❌ Mal: Una página de inicio con “¡Energía Solar!” y una foto genérica.

✅ Bien:

Una página titulada “Cómo Funcionan los Paneles Solares en 2025”, con:

Una explicación detallada
Un diagrama (alt text: "Flujo de energía en un panel solar")
Una sección de preguntas frecuentes (FAQs)
Un enlace a una discusión reciente en X sobre innovaciones en energía solar

En Resumen

Este cambio significa que simplemente seguir los métodos antiguos de SEO—como el uso excesivo de palabras clave o solo mejorar la velocidad de carga—ya no es suficiente.

Las IAs de hoy buscan:

✅ Contexto

✅ Profundidad

✅ Valor claro

Si quieres que tu contenido sea visible en esta nueva era de búsquedas impulsadas por IA, es hora de adaptarse, evolucionar y optimizar para el futuro.

Conéctate

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💻 GitHub: https://github.com/cpTheAideveloper

How can AI find my WEBSITE?

Carlos Polanco — Thu, 20 Feb 2025 16:59:19 +0000

Have you noticed that we’re browsing the traditional web less and less? Instead of opening your browser and typing in Google, you simply ask your AI assistant:

“What is SEO and how can it help my website?”

In seconds, you receive a clear and concise answer—without having to scroll through multiple pages.

That’s when my concern began. As a developer, I’ve always relied on traditional SEO strategies to increase my site’s visibility. But with the rise of AI that can search and understand content in a semantic way, I started to wonder:

How do AIs search the web, and how can I make sure my site is optimized for them?

The Dual Training Challenge

At first, I thought improving my traditional SEO would be enough. I optimized keywords, improved loading speed, and ensured a clean link structure.

A small step forward—but soon, I realized it wasn’t enough. AIs don’t just look for keywords; they look for context, relevance, and depth. It wasn’t just about speaking Google’s language, but rather about speaking AI’s language.

This meant creating richer content, implementing structured data, and ensuring that each page provided real and relevant value.

The Revelation: A New Strategy Needed

Consulting with different AI models revealed a fundamental truth:

1. Quality and Relevant Content

Make sure your content answers users’ questions. Think about how people phrase their queries and use natural language that reflects that.

✅ Instead of just writing about “SEO”, create a complete guide that answers specific questions like:

How does SEO work in 2025?

2. Structured Data

Implement schema markup using JSON-LD to help AIs understand the context of your content.

🛠️ This improves eligibility for rich snippets and voice search results.

3. Freshness and Authority of Content

AIs prioritize up-to-date and trustworthy content.

🔄 Regularly update your content and build backlinks from reputable sites to signal trust and authority.

4. User Engagement

AIs track user interaction. Metrics like dwell time and bounce rate tell them whether your content is valuable.

📊 Encourage engagement by making your content interactive, well-structured, and easy to navigate.

A Practical Example

Let’s say you have a site about solar energy:

❌ Bad: A homepage with “Solar Energy!” and a generic stock photo.

✅ Good:

A page titled “How Solar Panels Work in 2025”, featuring:

A detailed explanation
A diagram (alt text: "Energy flow in a solar panel")
A FAQs section
A link to a recent discussion on X about solar innovations

To Sum Up

This shift means that simply following old SEO methods—like keyword stuffing or just optimizing for page speed—is no longer enough.

Today’s AIs seek:

✅ Context

✅ Depth

✅ Clear value

If you want your content to be seen in this AI-driven search landscape, it’s time to adapt, evolve, and optimize for the future.

YouTube: https://www.youtube.com/@theaideveloper

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¿Realmente es “Gratis” Llevar tu Modelo de IA de tu PC a la Web?

Carlos Polanco — Tue, 18 Feb 2025 11:32:19 +0000

La revolución de la inteligencia artificial ha dado paso a modelos cada vez más potentes como DeepSeek, Qwen, Llama y herramientas como Ollama. Estas permiten ejecutar modelos directamente en tu computador. La emoción de correr estos modelos localmente, incluso en equipos modestos con versiones de 1B a 7B parámetros, es innegable. Pero, ¿qué sucede cuando decides desplegarlos en la web? La verdad implica considerar hardware, infraestructura, escalabilidad y muchos otros factores que podrían sorprenderte.

La Realidad Detrás de la Ejecución de IA: Local vs. Web

Acto 1: El Sueño Local

Imagina esto: estás trabajando en tu salón, desarrollando tu propio modelo de IA. Al principio, todo parece sencillo y económico.

Modelos Pequeños (7B parámetros):
- Requisitos: CPU x86-64 con soporte AVX2, 8–16 GB de RAM y, opcionalmente, una GPU básica (ej. RTX 3060).
- Ventajas: Bajo costo y facilidad para pruebas o cargas no críticas.
Modelos Medianos (14B parámetros):
- Requisitos: CPU x86-64 con AVX2, 16–32 GB de RAM y una GPU de gama media (ej. RTX 3080/3070).
- Ventajas: Buen rendimiento para aplicaciones más especializadas sin requerir hardware de alta gama.

Todo marcha bien hasta que decides que tu creación merece estar disponible para el mundo.

Acto 2: El Choque con la Web

"Pensé que migrar a la web sería sencillo… PERO me equivoqué." Recuerdo la primera vez que intenté desplegar mi modelo en línea.

Costo de Infraestructura en la Nube:
- GPU de Alta Gama:
- Costo: Aproximadamente $3–$4 USD por hora, lo que suma entre ~$2,200 y ~$2,900 USD mensuales si se utilizan continuamente.
- Instancias On-Demand en Grandes Proveedores (AWS, GCP, Azure):
- Costo: Alrededor de $30–$40 USD por hora, alcanzando hasta ~$28,800 USD mensuales.
- Ejecución Solo en CPU:
- Costo: Entre $1 y $1.5 USD por hora, resultando en ~$720 a ~$1,080 USD mensuales, pero con inferencia más lenta.

¿Te imaginas pagar hasta $28,800 al mes? Y esto es solo el comienzo...

Costos Adicionales:
- Load Balancers: Para manejar el tráfico de usuarios.
- Seguridad: Implementación de firewalls y protección DDoS.
- Escalabilidad: Incrementos en costos durante picos de demanda.

La carga de estos costos transforma tu sueño en una pesadilla financiera.

Acto 3: La Transformación y el Nuevo Normal

Después de muchas noches en vela y cálculos interminables, encontré un equilibrio:

Rendimiento Óptimo vs. Soluciones Económicas:
- Con GPUs potentes: Inferencia rápida y adecuada para aplicaciones en tiempo real.
- Con GPUs de gama baja o CPU: Reducción de costos, pero con velocidad de respuesta limitada.

Finalmente, entendí que cada decisión tiene su precio y su valor.

¿Qué Significa Todo Esto para Ti?

Antes de dar el salto a la web, considera:

Prototipos y pruebas locales: Un PC o una máquina modesta puede ser suficiente.
Aplicaciones en producción en la web: Necesitas invertir en infraestructura, optimización y seguridad para garantizar una experiencia de usuario de calidad.

Imagínate detectar estos costos ocultos antes de empezar, evitando sorpresas desagradables y asegurando que tu inversión realmente se traduzca en éxito.

La Decisión Final: ¿Estás Listo para el Desafío?

Migrar tu modelo de IA a la web no es tan “gratis” como podría parecer inicialmente. Evaluar el equilibrio entre costo y rendimiento, la escalabilidad, la seguridad y la optimización del modelo es crucial para evitar sorpresas y garantizar una implementación exitosa.

¿Te animas a explorar más sobre alguna de estas opciones? ¡Comparte tus dudas o experiencias en los comentarios y juntos descubramos el mejor camino para tu proyecto de IA!

¡Sigue Aprendiendo!

Si estás listo para dar el siguiente paso, aquí tienes algunos recursos útiles para profundizar en costos y estrategias de despliegue de modelos de IA en la web:

YouTube: https://www.youtube.com/@theaideveloper
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Twitter: https://x.com/cpaideveloper
TikTok: https://www.tiktok.com/@codingnutella
LinkedIn: https://www.linkedin.com/company/theaidevelopercp/
GitHub: https://github.com/cpTheAideveloper

Can You Really Run AI on Your Own PC?

Carlos Polanco — Thu, 13 Feb 2025 18:46:31 +0000

Ever wondered if you can run AI models right from your home computer? Many people think all you need is a "decent GPU," but the reality is a bit more complicated. Sure, smaller AI models might work with some tweaks, but handling larger models or achieving faster speeds typically requires a PC specifically built for AI tasks. Let’s dive into what you need to know to see if your setup can handle the challenge.

Why It’s Not So Easy

AI models today are incredibly large, sometimes featuring billions of "parameters." Imagine each parameter as a tiny adjustment knob that helps the AI understand language or recognize images better. The more knobs there are, the smarter the AI can be. However, this also means you need a lot of memory and processing power to keep everything running smoothly.

VRAM (Video RAM): This is the memory on your graphics card, handling all the visual data.
System RAM: This is your computer’s main memory, managed by the CPU (the brain of your computer).

If an AI model requires more VRAM than your GPU has, it might not run at all or could become painfully slow. In some cases, your system might even crash. That’s why it’s crucial to choose models that match your hardware or find ways to make them smaller.

Example PC Setups by Model Size

Here’s a simple guide to what different AI model sizes need to run smoothly. These are approximate recommendations to help you get a "snappy" experience without long wait times.

1.5B Parameters

CPU-Only: A decent midrange CPU with at least 8 GB of RAM can handle smaller models without much hassle.
GPU: Even a basic graphics card, like an RTX 2060 with 6+ GB VRAM, can boost performance.

7B Parameters

GPU:
- 8-bit: Requires around 7 GB VRAM. A GPU like the RTX 3060 with 8–12 GB should work fine.
- 4-bit: Needs about 3.5 GB VRAM. A GPU with 4–6 GB might be sufficient.
CPU:
- At least 16 GB of RAM is recommended for smooth performance. With only 8 GB, you might struggle if you’re running other applications.

14B Parameters

GPU:
- 8-bit: About 14 GB VRAM is needed, so aim for cards like the RTX 3090 or higher.
- 4-bit: Around 7 GB VRAM, which fits on an 8–10 GB card but expect slower performance.
CPU:
- 32 GB of RAM helps manage the entire model or allows for some parts to be offloaded.

32B Parameters

GPU:
- 8-bit: Approximately 32 GB VRAM is required, typically found on professional-grade cards.
- 4-bit: About 16 GB VRAM, so a high-end card like the RTX 3090 or 4090 might handle it with some help from the CPU.
CPU:
- 64 GB system RAM is ideal if you’re relying mostly on the CPU.

70B Parameters

GPU:
- 8-bit: Around 70 GB VRAM, which usually means using multiple GPUs or a top-tier card.
- 4-bit: Roughly 35 GB VRAM, likely needing two high-memory cards or a single super high-capacity GPU.
CPU:
- 128 GB of RAM is recommended, but even then, it will run very slowly.

Other Important Factors

Speed and Performance

Even if your GPU has enough VRAM, a higher-end GPU like the RTX 4090 will typically generate text faster than a mid-range one. This is because it has more processing cores and can handle data more quickly.

Quantization Trade-Offs

Using 4-bit or 8-bit quantization can significantly reduce memory usage, but it might slightly decrease the AI model’s accuracy. For everyday tasks, this trade-off is usually minimal.

Context Window

If you give the AI a long input, it needs more memory to keep track of everything. Models with larger context windows (like 4K or 8K tokens) will use more VRAM or RAM, so keeping your inputs concise helps.

Ollama vs. Other Tools

Ollama: This tool can offload some processing to your CPU, which is helpful if your GPU doesn’t have enough VRAM.
On macOS: Ollama uses Apple’s unified memory directly.
On Windows/Linux: You’ll need the right drivers (like NVIDIA CUDA), which might require some setup.

Multi-GPU or Distributed Setups

Yes, you can spread a large AI model across multiple GPUs, but setting this up can be tricky. If you only have one GPU with less than 16 GB VRAM, you’ll likely need strong quantization and some CPU offloading to run models bigger than 14B.

Practical Summaries by Model Size

1.5B: Runs on almost any modern PC with 4+ GB VRAM or 8 GB RAM.
7B: Needs a GPU with around 6–8 GB VRAM for 8-bit or 3.5 GB for 4-bit. Alternatively, 16 GB RAM for CPU-only.
14B: Requires at least 8–10 GB VRAM for 4-bit or around 16 GB for 8-bit. CPU might need 32 GB RAM.
32B: Typically needs 16–32 GB VRAM or 64 GB system RAM for CPU-only.
70B: Demands multi-GPU setups or a very high-end GPU with 80 GB VRAM. CPU-only setups would need around 128 GB RAM but would run very slowly.

Final Thoughts: Is It Worth It?

If you’re just starting out, smaller models like 1.5B to 7B are a great way to explore AI without overloading your computer. They let you experiment locally, avoid monthly cloud costs, and get quick feedback—assuming your hardware can handle it. But as you move to larger models (14B+), you’ll need a more powerful setup to keep things running smoothly.

The upside? No ongoing cloud fees, more hands-on experimentation, and faster iterations if your PC is up to the task. The downside? If your hardware doesn’t meet the requirements, you might face slow speeds or spend a lot of time troubleshooting. Running AI at home is all about balancing speed, accuracy, and cost.

Stay curious and keep experimenting! For more resources, check out:

Guides
Projects
YouTube: The AI Developer
LinkedIn: The AI Developer
GitHub: The AI Developer