DEV Community: Jim

How Michael Vicente’s RAG Project Teach Me About Building Smarter AI?

Jim — Tue, 21 Jul 2026 18:51:12 +0000

I recently came across a weekend project shared by Michael Vicente, and it changed how I think about building AI-powered applications.

Michael built a Retrieval-Augmented Generation system for AIO Growth, connecting ChatGPT to a database containing more than 5,000 AI tools. The goal was to provide fast and personalized tool recommendations without sending the entire database to the AI every time someone submitted a question.

At first, the idea of connecting AI to thousands of tools sounded like it would require sending a massive amount of information with every request. That would consume more tokens, increase costs, and potentially make the application slower.

Michael approached the problem differently.

Instead of giving ChatGPT access to everything at once, his system follows three focused steps.

First, ChatGPT identifies what the user is looking for. Next, MongoDB searches the database and retrieves only 15 to 20 relevant tools. Finally, ChatGPT uses that smaller selection to create personalized recommendations.

User submits a question
        ↓
ChatGPT understands the intent
        ↓
MongoDB retrieves relevant tools
        ↓
ChatGPT creates personalized recommendations

What caught my attention was not only how the system worked, but also the results Michael reported.

His approach reduced the cost per query by 93 percent, from approximately $0.0008 to $0.00005. It also made responses 40 percent faster, with an average response time of around 1.2 seconds.

He used GPT-4o-mini for cost-effective reasoning, MongoDB for semantic filtering, and compact tool summaries that reduced token usage by approximately 60 percent. Guests receive access to Smart Search, while signed-in users can use the full conversational AI experience.

The biggest lesson I learned from Michael’s discovery is that building a smarter AI application does not always mean sending more information to the model.

It means sending the right information.

Before learning more about RAG, I thought an AI system would provide better answers if it received as much data as possible. Michael’s project showed me that too much unnecessary information can increase costs and slow down the experience.

Retrieval makes the process more focused. Instead of asking the AI to search through thousands of possibilities, the system first selects a small group of relevant options. The AI can then concentrate on explaining and personalizing those results.

I also learned that building with AI is not only about choosing the most powerful model. The structure around the model matters just as much.

The database, retrieval process, summaries, access levels, and user experience all contribute to the final result. A smaller and more affordable model can still deliver a strong experience when it receives relevant and well-organized context.

Michael’s project also reminded me that useful technology often begins with a practical question:

How can this be made faster, more relevant, and less expensive?

As someone who is currently learning coding and automation, this gave me a clearer understanding of how AI applications can be designed for real-world use.

It is not simply about connecting an application to ChatGPT. It is about deciding what information the model needs, when it needs it, and how to deliver that information efficiently.

Michael’s project made RAG easier for me to understand. More importantly, it showed me that optimization is not always about adding more powerful technology.

Sometimes, the smartest solution is reducing the amount of work the AI needs to do.

Read Michael Vicente’s original project post.

I Used to Think Coding Was Only for Programmers

Jim — Tue, 21 Jul 2026 18:43:03 +0000

For a long time, I believed coding was only for people who studied computer science or worked as professional developers.

Whenever I saw a screen filled with code, it looked like a completely different language. There were brackets, symbols, functions, and terms I did not understand. I assumed learning it would require years of study before I could create anything useful.

That changed when I encountered a repetitive task at work.

I was regularly copying information from a spreadsheet, checking each row, preparing an email, sending it, and updating the status manually. The work was manageable, but completing the same process repeatedly took time and left room for mistakes.

I started wondering if the spreadsheet could do some of the work for me.

That question led me to Google Apps Script.

At first, I did not even know where to begin. I understood the result I wanted, but I did not know how to translate it into code. I could explain the process clearly to another person, but explaining it to a computer felt different.

AI became my starting point.

I described the task and asked it to create a script. Within seconds, it gave me several lines of code. I copied them, ran the script, and immediately received an error.

My first reaction was frustration. I had expected the code to work because it looked complete. But I soon realized that generated code was not automatically working code.

I went back and explained the error. AI suggested a change, so I tested it again. Another issue appeared. I repeated the process until the automation finally worked.

The moment it worked, something changed in the way I viewed coding.

I did not suddenly become a programmer, but I had created something useful. A task that previously required several manual steps could now happen automatically.

I became curious about what else I could build.

I started experimenting with confirmation emails, timestamps, form submissions, missing-data checks, and automatic reports. Each project introduced me to a new concept. I learned about variables because I needed to store information. I learned about conditions because the script needed to make decisions. I learned about triggers because the automation needed to know when to run.

Instead of learning these concepts only through definitions, I learned them while solving actual problems.

There were still many mistakes. Sometimes I used the wrong column number. Sometimes a script worked during testing but failed when another person used the spreadsheet differently. Sometimes I spent hours fixing a problem caused by one missing character.

Those experiences taught me to read error messages, test different situations, and ask more specific questions.

They also taught me not to rely completely on AI.

AI can provide a starting point, explain unfamiliar code, and suggest possible fixes. However, I still need to understand what the script is doing. If I copy code without understanding it, I will not know what to do when the workflow changes or something stops working.

I am still learning, and there is a lot I do not know. But I no longer see coding as something reserved only for professional developers.

Coding can also be a practical tool for people who want to solve everyday problems, improve workflows, and save time.

Sometimes, learning does not begin with memorizing a programming language. It begins with noticing a problem and becoming curious enough to ask:

Is there a better way to do this?

How AI Helped Me Discover Automation

Jim — Tue, 21 Jul 2026 18:39:53 +0000

It started with one simple question:

Why am I still doing this manually?

I was working on a spreadsheet, checking information row by row, sending emails, and updating statuses. The process was not difficult, but it was repetitive. One small mistake could mean sending incorrect information, overlooking a request, or forgetting to update a row.

I knew there had to be an easier way.

I wanted the spreadsheet to detect when a status changed to Sent, find the email address in the same row, send the correct message, and add a timestamp after the email was sent.

The idea sounded simple in my head. The problem was that I did not know how to build it. I was still learning how to code, so I asked AI for help.

My first prompt was something like:

Create a script that sends an email from Google Sheets.

AI immediately generated a script. It looked impressive, but it did not work the way I expected. The script checked the wrong column, used information from the wrong cells, and failed when some required details were missing.

That was when I realized the problem was not only the code. My instructions were too vague.

I tried again, but this time I described the entire process:

When the status in Column G changes to "Sent," get the email
address from Column E and send a confirmation email.

After the email is sent successfully, add a timestamp to Column H.

Do not send the email if any required information is missing.

The result was much closer to what I needed.

When I changed the status to Sent and received the email automatically, I felt genuinely excited. It was only a small automation, but it removed several manual steps from the process.

After building that first workflow, I started noticing repetitive tasks everywhere.

A form submission could automatically send a confirmation email. A spreadsheet could detect missing information before processing a request. A completed action could record the date and time. Reports could be organized without manually copying every row.

Tasks that previously took several minutes could happen in seconds.

AI helped me understand that many automations follow a simple structure:

Trigger → Condition → Action

Something happens, the automation checks whether certain requirements are met, and then it completes the assigned task.

Thinking this way made automation easier for me to understand. Before writing any code, I could first map out what needed to happen.

Not every script worked on the first try. Some produced errors. Others failed when a cell was empty or when someone entered a status with extra spaces. There were also times when fixing one issue created another.

Those problems taught me that AI can help generate code, but it cannot replace understanding and testing.

Now, I ask questions like:

What could cause this script to fail?
What happens when a required cell is empty?
How can I prevent duplicate emails?
Can this handle multiple edited rows?
Can you explain each section in simple language?

These questions help me learn instead of simply copying and pasting code.

I also learned that not every task should be automated. Some tasks still require human judgment, personal communication, or careful review.

For me, automation makes the most sense when a task is repetitive, based on clear rules, completed frequently, time-consuming, or prone to manual errors.

The goal is not to remove people from the process. It is to reduce repetitive work so people can focus on tasks that require attention, creativity, and better decision-making.

I did not begin this journey with the goal of becoming an expert programmer. I simply wanted to solve one repetitive problem.

That problem encouraged me to learn Google Apps Script, understand basic JavaScript, and explore how AI could support my learning.

I am still learning, testing, and making mistakes. However, I now look at repetitive processes differently.

Whenever I find myself completing the same steps again and again, I stop and ask:

Can this be automated?

What repetitive task made you interested in automation?