tgroenwals shared this post · Aug 19

Luís Rodrigues

Aug 11 · archived Aug 19

Most people hear "AI".
And think it's one thing.

There are seven layers. Each one wraps around the last, like Russian nesting dolls.

Image by my friend Will Stewart, MBA , give him a follow.

𝗖𝗹𝗮𝘀𝘀𝗶𝗰𝗮𝗹 𝗔𝗜

Pure logic. “If this, then that”.

Symbolic AI and Expert Systems that follow rigid rules to make decisions.
It doesn't learn, it just follows orders.

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴

We stopped writing rules and started using math.

Algorithms analyze data to find patterns.
They classify, predict, and optimize based on history, not hard-coded instructions.

𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀…

Nelson Uzenabor The transition from prompting to delegating is where the real operational leverage happens. Firmly focused on the Agentic AI space right now, building the governance, connections, and workflows needed to make autonomous execution viable in production.

Luís Rodrigues Author Well said, Nelson. Moving from prompts to execution changes how teams think about AI.

Ashley Nicholson Understanding the layers helps cut through the noise around AI. Real progress comes from knowing which level solves the problem, not just chasing the newest tool.

Luís Rodrigues Author Ashley. Yes, the right solution starts with knowing what problem you are actually solving.

tgroenwals shared this post · Jul 28

Luís Rodrigues

Jul 25 · archived Jul 28

Most people using AI daily can't explain 12 basic terms.

Not obscure ones, the ones that show up in every product announcement, every board deck, every job description.

I picked 12 that matter right now.
Not 12 for ML engineers.

12 for anyone who builds, buys, or decides anything involving AI in 2026.

Grouped them into four buckets:

→ The Foundations: LLM, Token, Context Window

The building blocks, what AI is, how it reads, and how much it can hold in its head.

→ How AI Learns: Fine-tuning, RAG, Hallucination

How models get smarter, stay grounded, and where they still fail.

Ashley Nicholson Guardrails deserve far more attention. Trust grows when people know AI can handle the expected work and fail safely when something unexpected happens.

Luís Rodrigues Author Well said, Ashley. Strong guardrails make the difference between testing AI and using it every day.

Shiyal Suresh Excellent overview. I'd argue the most important shift isn't learning these terms individually it's understanding how they work together. LLMs provide intelligence, RAG provides trusted knowledge, MCP enables connectivity, agents drive execution, and guardrails ensure reliability. Production AI succeeds when all of these layers operate as a cohesive system, not as isolated technologies.

tgroenwals shared this post · Jun 22

Luís Rodrigues

Jun 21 · archived Jun 22

Most people think Agentic AI is just "ChatGPT + tools"

They're wrong.

This diagram shows the full stack.

Five layers. Each one matters.

Most failures happen in layers four and five. Not in the models.

𝟭/ 𝗔𝗜 & 𝗠𝗟 (𝗧𝗵𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻)
Data becomes decisions here.

Classical learning. Supervised, unsupervised, reinforcement.

𝟮/ 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗧𝗵𝗲 𝗘𝗻𝗴𝗶𝗻𝗲)
Neural networks. Transformers.

The machinery learns patterns at scale.

𝟯/ 𝗚𝗲𝗻𝗔𝗜 (𝗧𝗵𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗲)
LLMs generate content. RAG pulls context.

Multimodal models handle text, image, audio, video.
The output layer.

Steven Jonathan This is a fantastic breakdown. The emphasis on layers four and five resonates deeply with our own experience in deploying AI agents for sales outreach. We have found that robust governance, observability, and failure recovery are not just nice-to-haves; they are the critical difference between a system that builds trust and one that damages a brand. Architecture truly is the unsung hero of reliable autonomy.

Luís Rodrigues Author Absolutely, Steven. Architecture quietly determines whether autonomy is safe or risky.

tgroenwals shared this post · Jun 6

Luís Rodrigues

Jun 4 · archived Jun 4

Most companies are building AI wrong.
Here's why.

Most people treat AI like one thing.

It's five very different capabilities that do very different jobs.
Confusing them is probably why half the implementations fail.

Here's my view:

𝟭. 𝗣𝗥𝗘𝗗𝗜𝗖𝗧𝗜𝗩𝗘 𝗔𝗜 & 𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚
The boring stuff that makes money.

It reads your past data, flags risks, forecasts demand, and spots fraud. If you don't have this, you aren't ready for the rest.

𝟮. 𝗣𝗘𝗥𝗖𝗘𝗣𝗧𝗜𝗢𝗡 𝗔𝗜 (𝗗𝗘𝗘𝗣 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚)
Giving your business eyes and ears…

1 / 2

Alex Barády without solid data foundations, advanced AI is just noise. Luís Jun 4 1 like

Tash Durkins, CPC Most companies are not failing at AI because the tools are bad,

They are failing because they are trying to build Level 5 systems on top of Level 1 data. Jun 4 1 like

Author posts

Most people using AI daily can't explain 12 basic terms.

Most people think Agentic AI is just "ChatGPT + tools"