Phi 4 available on Ollama

I'm more confused now. If this is a tough and high-value task, we would not use gpt-4o-mini on its own, eg, add more steps like a verifier & retry, or just do gpt-4o to begin with, and would more seriously consider fine-tuning in addition to the prompt engineering. The blog argued against that, but maybe I read too quickly.

And agreed, people expect $ they invest into computer systems to do much better than their bad & avg employees. AI systems get the added challenge where they must do ~100% on what non-AI rules would catch ("why are you using AI?") + extra lift from AI ("what did this add?"). We generally get evaluated on matching experts (low bar), and exceeding them (high bar). Comparing to average staff is, frustratingly, a breakout.

Each scenario is different obviously..

'it varies' b/c we do everything from an interactive analytics chat agent (loiue.ai UI) to data-intensive continuous-monitoring (louie.ai pipelines) to one-off customer assists like $B court cases

1. Common themes in our development-time loop:

* We don't do synthetic data. We do real data or anonymized data. When we lack data, we go and get some. That may mean paying people, doing it ourselves, setting up simulation environments, etc.

* We start with synthetic judges, esp for scale tasks that are simple and thus considering smaller models like gpt-4o-mini (the topic here). Before we worry about expert agreement, we worry about gpt-4o agreement, and make evals that cover concerns like sample size and class imbalance...

* ... When the task is high value, e.g., tied closely to a paying customer deliverable or core product workflow, we invest more on expert evals, making calls like on how many experts and of what caliber. Informally, we've learned multiple of our teammates, despite good at what they do, can be lousy experts, while others are known for precision, even if not data people (ex: our field staff can be great!). Likewise, we hire subject matter experts as full-timers (ex: former europol/fbi equivs!), source as contractors, and, partner with our customers here.

* After a year+ of prompt engineering with different tasks, models, data, and prompt styles, there's a lot of rote tricks & standard practices we know. Most are 'static' -- you can audit a prompt for gotchas & top examples to fill in -- and a smaller number are like in the OP's suggestion of dynamic prompts where we include elements like RAG.

On the last point, it seems incredibly automatable, so I keep trying tools. I've found automatic prompt optimizers like dspy to be disappointing in being unable to match what our prompt engineers can do here: they did not do better then prompts we wrote as experts with bare bones iteration, and leaning into the tools failed to get noticeable lift. I don't think this is inherent, just they're probably eval'ing against people we would consider trainees. Ex: I see what stanford medical fellows+phds are doing for their genai publications, and they would probably benefit from dspy if it was easier, but again, we would classify them as 'interns' wrt the quality of prompt engineering I see them doing behind-the-scenes. I'm optimistic that by 2026, tools here will be useful for skilled AI engineers too, just they're not there yet.

2. It's a lot more murky when we get into online+active learning loops for LLMs & agentic pipelines.

E.g., louie.ai works with live operational databases, where there is a lot wrt people + systems you can learn from, and issues like databases changing, differences in role & expertise, data privacy, adverserial data, and even the workflows change. Another area we deal with is data streams where the physical realities they're working with changes (questions+answers about logs, news, social, etc).

IMO these are a lot harder and one of the areas a lot of our 2025 energy is going. Conversely, 'automatic prompt engineering' seems like something PhDs can make big strides in a vacuum...

As a bit minor criticism "Tasks Completed (Time)" is hard to evaluate without the time intervals or units. I'm not sure if it should just be "Time"?

I should emphasize that I really don't think the dystopian version of this is likely to happen - the one where "AGI/ASI" puts every human out of work and society collapses.

Human beings have agency, and we are very good at rolling with the punches. We've survived waves of automation for hundreds of years. I'm much more confident that we will continue to find ways to use these things as tools that elevate us, not replace us.

I really hope the dystopian version doesn't come to pass!

I asked the same question a few days back and I'm keeping the responses here: https://bsky.app/profile/potato.horse/post/3lejngewfmc2n

For reasoning: qwq:latest (19G file)

For coding: qwen2.5-coder:14b (9G file)

Misc. experiments, runs fast: llama3.2:latest )2 G file)

The enshitification trend seems pretty dominant, and pretty bad for users / good for investors

Meta's entire business model is to own users and their content.

Whether it be Facebook, Instagram, Threads, Messenger, WhatsApp, etc. their focus is to acquire users, keep them in their platforms, and own their content - because /human attention is fundamentally valuable/.

Meta owns 40% of the most popular social media platforms today, but their attention economies face great threats: YouTube, TikTok, Telegram, WeChat, and many more threaten to unseat them every year.

Most importantly, the quality of content on these platforms greatly influences their popularity. If Meta can accelerate AI development in all forms, then it means the content quality across all apps/platforms can be equalized - video on YouTube or TikTok will be no more high quality than on Facebook or Instagram. Messages on Threads will be no more engaging than that on Twitter. Their recent experiments with AI generated profiles[0] signals this is the case.

Once content quality - and luring creators to your platform - are neutralized as business challenges that affect end users lurking on the platform and how effectively they can be retained, then it becomes easier for Meta to retain any user that enters their platforms and gain an effective attention monopoly without needing to continue to buy apps that could otherwise succeed theirs.

And so, it is in their benefit to give away their models 'for free', 'speed up' the industry's development efforts in general, de-risk other companies surpassing their efforts, etc.

[0] https://thebaynet.com/meta-faces-backlash-over-ai-generated-...

To be fair, OpenAI's products are not really models, they are... products. So it's debatable if they really do have anything special.

I don't really think they do, because to me it seemed pretty much since GPT-1, that having callbacks to run python and query google, having "inner dialog" before summarizing an answer and a dozen more simple improvements like this are quite obvious things to do, that nobody just actually implemented (yet). And if some of them are not obvious per se, they are pretty obvious in the hindsight. But, yeah, it's debatable.

I must admit though, that I doubt that this obvious weakness is not obvious to the stakeholders. I have no idea what the plan is, maybe what they gonna have that Anthropic doesn't is gonna be a nuclear reactor. Like, honestly, all we are pretending to be forward-thinking analysts here, but in reality I couldn't figure out that Musk's "investment" into Twitter is literally politics at the time of it happening. Even though I was sure there is some plan, I couldn't say what it is, and I don't remember anybody in these threads expressing clearly enough what is quite obvious in the hindsight. Neither did all these people like Matt Levine, who are actually paid for their shitposting: I mostly remember them making fun of Musk "doing stupid stuff and finding out" and calling it a "toy".

I use OpenAI not just because it has decent models that work decently by default but because I don't need to care on how to setup a model on a cloud provider and their API is straight forward. They are quite affordable too (e.g. TTS is one of the cheapest I found for its quality)

I could switch to a different provider if I needed to maybe with cheaper pricing or better models but that doesn't mean OpenAI doesn't offer a "product".

Open AI is the only company that really matters in the consumer conversational AI space.

Their unique value-adds are the Chat GPT brand, being the "default destination" when people want AI, as well as all the "extra features" they add on top of raw LLMs, like the ability to do internet searches, recall facts about you from previous conversations, present data in a nice, interactive way by writing a react app, call down to Python or Wolfram Alpha for arithmetic etc.

I wouldn't be surprised if they eventually stop developing their own models and start using the best ones available at any given time.

> It suggests that OpenAI has a very fragile business model

That is the reason they are making products so that people stay on the platform.

There are a lot of interesting options. Gemini 2 Flash isn't ready yet (the current limits are 10 RPM and 1500 RPD) but it could definitely work. An alternative might be using a fine tuned model - I've heard good things about OpenAI fine tuning with even a few examples.

Honestly, the fact that you used an LLM to grade the answers at all is enough to make me discount your results entirely. That it showed obvious preference to the model with which it shares weights is just a symptom of the core problem, which is that you had to pick a model to trust before you even ran the benchmarks.

The only judges that matter at this stage are humans. Maybe someday when we have models that humans agree are reliably good you could use them to judge lesser-but-cheaper models.