(Updated on 15th August 2025)
Two weeks ago, I found Vicuna, “an open-source chatbot impressing GPT-4 with 90%* ChatGPT quality”. I knew to take that 90% stat with a pinch of salt but wanted to try it.
For my prompt, I tried something I’ve been using on GPT3.5:
And the output:
Sounds believable… but it’s not all true. There isn’t a flying eagle nor a rocky shore on the back. The rest is generic and subjective. It certainly wasn’t in the style of the person I used.[1]
Now to test the chatbot element. I prompted Vicuna to reword the previous output “in the style of Don King”. I wanted something ostentatious and my mind went to Don King as I’ve used boxing related prompts in the past. Here’s the result:
Out. Standing(ly bad).
It’s a very bad caricature of Don King but it made me laugh because it was so bad. I could see a crap (probably white, almost certainly cis male) comedian try this style of comedy and flop.
More on Vicuna
Vicuna published a full breakdown of the model, it’s advantages, and disadvantages. The tl;dr: it’s a good open-source alternative but it could never be as good as Bard/any GPT model. Unfortunately, the dataset isn’t available and there are no plans to release it which is a shame and undermines the open-sourceness of it a little, but it might also be a legal thing; I dunno.
A different test
I also used it to try and find a link between two randomly selected tags from my blogs. I got ‘bell hooks’ and ‘Tony Hawk’ and this is what it said:
Coherent and fairly true. I wouldn’t have expected an open-source model to find a deeper connection than I would have found besides them both being American.
I also tried Teenage Mutant Ninja Turtles and jewelry:
Again, plausible. The last part in particular inspired me to find this piece of TMNT-themed jewelry which I blogged about. And that’s the beauty of this tech. To paraphrase Joe MacMillan from Halt and Catch Fire, “AI isn’t the thing; it’s the thing that gets you to the thing.” AI can’t give me a finished product but it can certainly help me get there, alongside my ingenuity.
Caveats
As we saw in the very first prompt + output, hallucinations are still a big flaw and it gets bigger with open source models created from better closed-source models. The idea is to take what’s there and refine and pre-train it on high quality training data to perform specific tasks. That’s where this tech excels. Where you’ll find outliers and inconsistent performance is trying to turn this proverbial black box into a Tower of Babel but that isn’t stopping every AI startup from trying.
I tried another pair of entities to see how they were linked (Martin Luther King and Cuba) and the model’s output said MLK had visited Cuba in 1960. That didn’t happen. These things aren’t great for factual information, even when you hook them up to the internet—you know, that completely factual and never wrong platform of knowledge.
These aren’t particularly good prompts either, I must admit. I also haven’t run enough tests to get any significant evidence of anything and to be honest, I didn’t even a hypothesis since I was just testing stuff out.
If you’re trying to prove anything, for or against LLMs, you need a robust hypothesis and qualitiative and quantative tests. Prompts are only as “good” as the LLMs who fall in line for them.
More on AI
- /prompt-engineering.md
- /ai-is-a-lot-like-an-orange.md
- /future-coca-cola-slogans-ai.md
- /a-poem-about-how-search-engines-cant-interpret-poems.md
- Prompt Engineering is for Transactional Prompting
Iceberg Notes 🧊
1I didn’t expect perfection or 100% truth (and neither should you!)