My latest adventure with my RAG AI is using it to help me provide makeup tips on Reddit. After a few initial setbacks, my approach has been surprisingly successful, and my answers are receiving a lot of positive feedback. Over the last 36 hours, I have become one of the top, if not the top, karma earners on a major makeup-focused subreddit. Karma on Reddit reflects how much people appreciate your content. This is all the more notable since I know almost nothing about makeup.

To make these recommendations, I combine AI’s ability to deeply examine photos, along with my RAG AI which I trained¹ specifically on Makeup to create results for me. I then post the recommendation on Reddit, often changing the tone of the AI output before posting. Since I wouldn’t know blush from bronzer, I don’t alter the actual recommendations before posting.

Exploring AI Frontiers

This all happened in a very roundabout way. A few months ago, I asked a friend at a top AI company what she thought was the next big frontier in AI. She mentioned that while there are many amazing tools freely available and worked on by enthusiasts, there are few actually generating value for people. The next frontier is turning these cool tools into viable businesses. I’ve been aggressively exploring different ways to take this amazing technology and make it useful for people.

The Journey Sage Finder

I was talking to another friend who is an influencer in the use of MDMA for PTSD treatment. She noted how people kept asking her the same questions that were already covered in her videos, but because her content was so spread out, it was difficult to point people to the right places.

This seemed like something I could do easily with AI, and I was looking for a project. So, I pulled together The Journey Sage Finder tool to solve this issue for her. The results were good, far better than ChatGPT alone, with the benefit of directing the customer to the original source material.

This was a straightforward implementation called a “Naïve RAG” (Retrieval-Augmented Generation). It handled simple searches very effectively, bringing up the exact spot in the relevant video. For example, searching for “How does MDMA help PTSD?” gave perfect results. However, complex searches for which there wasn’t a close answer, such as “Is MDMA or psilocybin better for PTSD and related anxiety,” didn’t work as well. The answers tended to be long, usually six or seven paragraphs of in-depth information.

To handle complex queries I would have to develop my skills with query transformation and preprocessing as well as with answer retrieval.

Why Not Just Use ChatGPT?

One question I hear a lot is why bother building something when you can just use ChatGPT. For a product like the Journey Sage Finder, which is built specifically to find videos, the answer is obvious: ChatGPT never had access to those videos and so can’t be used to search them.

For some other projects, especially those using websites of publicly available information, it might seem like ChatGPT would be just as good, but my experience shows it isn’t. It isn’t even close. I think it has to do with signal versus noise. ChatGPT has so much to digest that it can’t beat an AI that is focused specifically on just one topic.

My Virtual College Advisor

The Journey Sage Finder worked well enough that I decided to start tackling a much larger project: a ChatGPT-based tool to help students find the perfect college for them. Numerous tools and websites already exist for finding colleges, but they use the same standard set of publicly available data. For instance, they all ask if you want to go to a public or private university, even though almost no one really knows or cares which are public. They might care about related things, such as a break for in-state tuition, but they don’t care a bit whether the school is public or private. Companies have this as a criteria simply because it’s data that’s easy for them to get and use, not because the customer wants it.

My idea is to combine readily available things people care about, such as tuition, ranking, and location, with hard-to-find or quantify items such as a good rugby team, a strong Shakespeare course, and a nice campus.

Pulling this together meant a lot of website crawling and a lot of work on the search to return good results from complex data. After a month of hard work and constant evaluations, I settled on a search that combines a hybrid vector index and text search with a tag search². In evaluations this worked very well, returning strong results.

The weakness is that for college most queries have many correct answers. For instance, if you ask for schools with strong academics and a good lacrosse team you might return 50 or more results (out of 6,000 schools in my database). To get better results we need more information about the students. I have various strategies to make that happen, but all that I learned about search results and web scraping led me down a different path, for now.

Early Attempts and Failures

When I started work on the Virtual College Advisor, instead of building a product, I focused on building a platform, a central computer system that could handle multiple different data sources and types of output. As such, all I needed was to be pointed to a source of information and get some details on the desired output, and I could quickly create a new search, complete with a basic user interface. For instance, my brother is an avid coin collector, and I created a Coin Finder, which answers very detailed questions about coins far more accurately than ChatGPT would and more quickly than a Google search while providing information with attribution from multiple sources.

These results were impressive enough that I was sure people would love this tool. I created a free-to-use tool based on the videos and writings of Alex Hormozi, an entrepreneur and influencer, and placed the finder on a heavily trafficked area of Reddit, a social media site. I was so worried about potential heavy volume that I prepurchased a large amount of AI bandwidth from OpenAI to handle what I hoped would be a heavy load.

Instead, in the first two days, a grand total of two people clicked on the link and looked at it. I later found out my sister was one of them! I decided that the problem must be that this subreddit wasn’t as active or as enthusiastic as I’d expected, so I tried a number of other free-to-use finders (for coin collectors, fishing, stock markets) on social media sites. All got almost no traffic. Logging showed that it wasn’t that people tried it and didn’t like it, but that they wouldn’t even try it. Of the few who used it, more than half clicked the like button or upvoted it.

Transition to Makeup

The search and the UI improved with every iteration. By now, my search had evolved to be able to take in pictures as well as text from the customers. I was convinced that results of this quality must have a place where people would welcome them. The AI continued to produce results that were high quality and long, six or seven paragraphs of rich and detailed information.

My sister suggested makeup might be a good topic for my product. She pointed out that social media is flooded with people asking questions about their makeup and products, often consisting of both a picture and related text. Dozens of questions a day poured in from people who really wanted answers, and tens of thousands of people read it daily. This seemed like a perfect spot, so I created and rolled out a special version of the product for makeup.

More Crashes and Burns

I made sure to launch in the middle of the day when traffic on Reddit is lowest, as I was again worried about my servers being overwhelmed. This time, just under a thousand people had it come up in their feed, and a single one clicked on it. One lone soul. I haven’t had the courage to ask my sister if it was her..

This led me to hypothesize that the problem must be having to take a link to my website that was turning people off. Instead, of being able to use it right on Reddit, they would have to go to another site via a link. Since Reddit doesn’t allow embedding pages, which would allow me to offer the tool right on Reddit, I decided I would take people’s questions, directly input them into my RAG AI myself, and I would post the answer to Reddit.

I was sure these results would be welcomed as they were long and detailed. Once again, I was, very, very wrong. The first ten results got a total of one upvote between them! And more than a dozen downvotes. In social media, if something doesn’t get likes or upvotes soon after creation, they quickly get deemphasized and stop showing up in people’s feeds.

In short, they were total failures.

Adjusting and Succeeding

At this point, I stopped and took a good look at the rest of the posts on the Reddit site. Most posts were one paragraph, or even a single sentence, whereas mine were six or seven paragraphs long. They also tended to focus on a single suggestion, whereas mine might focus on three or four or more. Also, many posts started out by saying how great the person looked, even if they didn’t.

So, I changed my AI to produce much shorter results and focus on only one or two things. Before posting to Reddit I added something that I liked about the person’s photo, when one was included, and I used these to reply to people’s posts.

Mind you, one thing I still didn’t know is whether these results made any sense or whether the AI was hallucinating like Timothy Leary on an LSD trip. I did have a few friends review early results, which they thought were good, but I had no idea just how strong the results were.

Quickly after I started this revise posting, it became clear that the results were correct and accurate. People liked my replies and found them useful. Sometimes they liked them a lot. In one day, my Karma went from 163 to over 726, more than it had risen in the entire two previous years. Almost all posts received strong thanks from the reader; several generated profuse accolades after they tried the recommendation and it worked for them.

In short, my RAG AI is producing posts that are accurate and of high quality.

What’s Next?

This might surprise you, but being a makeup recommendation expert isn’t a dream of mine. I’m looking to build cool systems that solve problems. Still, I really enjoy these postings as they seem to be helping people with things they find important. In just 24 hours of posting, I have learned a lot about the Reddit ecosystem and what works and doesn’t work. I have made many adjustments and I will detail more of my observations in another post.

From a business standpoint, I see several options. First, I could use this as the basis to build up a brand: find a makeup enthusiast with some writing skills and the willingness to put in the time (and even AI-enhanced, this does take time). Not only could they answer user questions, but we could use the AI to help us make videos on new topics. I could also approach an existing influencer and let my AI enhance them.

There’s also no need to limit this to makeup. I can easily add other recommendation systems (remember, I can make ones on new subjects in under an hour), find other people who want to be the ‘experts’ in an area, and teach them to use the AI to help them help out anywhere there are people with questions they struggle to get answered.

I’ll also work on how to make the self-service solution work better. Certainly, part of the problem is that I am a backend systems expert, not a UI expert, and so the UI is not as crisp and clean as it could be. While that’s a weak point, the bigger trouble was getting people to even try it.

Last, but certainly not least, I’ve built up my skills to be able to help any company looking to get better answers from their data. RAG AI seems simple, and Naïve RAG is, but to get good results from complex data is exceedingly challenging. I believe part of why Goldman Sachs says there is little return on investment for Generative AI, is because of software engineers vastly underestimating what it takes to make good results and because of consultants overpromising. Still, those companies who find the true experts to help them stand to reap outsized benefits.

Conclusion

My journey with RAG AI, from PTSD therapy tools to becoming a Reddit makeup guru, demonstrates the incredible versatility and potential of this technology. It’s not just about creating great answers quickly; it’s about adapting those answers to meet the specific needs of each community we serve. The success on Reddit validates our approach and proves that when AI is properly tuned, it can provide genuine value in even the most unexpected places.

Quality output is essential, but it’s only the start. Just table stakes. The real magic happens when we align that output with the culture and expectations of our audience. This experience has taught me that the best AI solutions are those that seamlessly integrate into existing communities.

It’s clear to me that the potential applications for RAG AI are boundless. The key lies in understanding the unique challenges of each field and crafting AI solutions that truly resonate with users. By continuing to innovate and adapt both the quality of the answers and the user experience, we can unlock the true potential of AI.

It’s been great fun exploring new frontiers, refining techniques, and discover novel ways to make AI an indispensable tool. There have been a lot of failures along the way, but each has taught something valuable that helped shape a better product and experience in the end.

The future of AI isn’t just about technological advancement; it’s about empowering people with knowledge tailored to their needs. As this makeup adventure shows, humans working closely with AI assisting, is much more powerful than either humans or AI alone.

Footnotes:

1. I use the word “Training” here because I think it’s more understandable. Technically, this isn’t actually “training” which would be a much more expensive and onerous process. Training or fine tuning a model on makeup might yield as good or better results but would be orders of magnitude more expensive to build and run and to keep up to date.
2. Tags generation is a preprocessing step I created custom in which videos and webpage content are examined by the AI, and the most likely elements for it to be searched for are extracted. This helps get around the noise to signal problem by highlighting important piece of information buried in a long video or web page.