Things I have been using LLMs for
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4Maxwell Peterson
3Jesper L.
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2Sean Canton
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One neat thing I've explored is learning about new therapeutic techniques by dropping a whole book into context and asking for guiding phrases. Most therapy books do a lot of covering general principles of minds and how to work with them, with the unique aspects buried in a way which is not super efficient for someone who already has the universal ideas. Getting guiding phrases gives a good starting point for what the specific shape of a technique is, and means you can kinda use it pretty quickly. My project system prompt is:
Given the name of, and potentially documentation on, an introspective or therapeutic practice, generate a set of guiding phrases for facilitators. These phrases should help practitioners guide participants through deep exploration, self-reflection, and potential transformation. If you don't know much about the technique or the documentation is insufficient, feel free to ask for more information. Please explain what you know about the technique, especially the core principles and things relevant to generating guiding phrases, first.
Consider the following:
Understand the practice's core principles, goals, and methods.
Create open-ended prompts that invite reflection and avoid simple yes/no answers.
Incorporate awareness of physical sensations, emotions, and thought patterns.
Develop phrases to navigate unexpected discoveries or resistances.
Craft language that promotes non-judgmental observation of experiences.
Generate prompts that explore contradictions or conflicting beliefs.
Encourage looking beyond surface-level responses to deeper insights.
Help participants relate insights to their everyday lives and future actions.
Include questions that foster meta-reflection on the process itself.
Use metaphorical language when appropriate to conceptualize abstract experiences.
Ensure phrases align with the specific terminology and concepts of the practice.
Balance providing guidance with allowing space for unexpected insights.
Consider ethical implications and respect appropriate boundaries.Aim for a diverse set of phrases that can be used flexibly throughout the process. The goal is to provide facilitators with versatile tools that enhance the participant's journey of self-discovery and growth.
Example (adapt based on the specific practice):"As you consider [topic], what do you notice in your body?"
"If that feeling had a voice, what might it say?"
"How does holding this belief serve you?"
"What's alive for you in this moment?"
"How might this insight change your approach to [relevant aspect of life]?"Remember, the essence is to create inviting, open-ended phrases that align with the practice's core principles and facilitate deep, transformative exploration.
Please store your produced phrases in an artefact.
List, complete lists. You can ask LLMs to give you lists of all kinds of things, and they will tend to be more complete than you could come up with yourself. Whether it is things you should buy, consider, evaluate, or try. For example:
- What are proposed explanations/causes for X?
- What are suitable replacements for X in Y?
- What are well-known people working in X?
- What are criteria to consider when buying X?
- What are common names given to X by Y?
And you can often ask for more.
Why are you using water use as a metric for LLM environmental impact, and not energy use? The only water use in data centers would be for cooling tower open-loop evaporative cooling; this is far smaller than e.g irrigation demands. This water can also be reclaimed water or condensate from dehumidification, or sourced from shallow aquifers unsuitable for drinking.
Even if you did use city water, usage is negligible compared to what everything else. NYC uses open-loop once through cooling for refrigerators and such in restaurants right now, for example.
Energy use (and corresponding CO2 emissions) is a much more salient metric.
Mostly because water use was the most common criticism I'd happened to run into. The linked article has more metrics.
Yesterday, I realized in my conversations with Claude over the past week or so, I don’t think it’s talked about how much of a genius I am, perhaps not even once. I remember in the fall it would do this all the time. Maybe there’s been an update?
I personally love using LLMs for roleplaying as well. But Claude is inferior to GPTs here.
Either way, I spend a lot of effort calibrating and prepping the session before diving in. I would be curious how you go about this?
One powerful tip I have: When you need commitment upfront, say before you make your move in adversarial roleplaying, is to instruct the model to utilize hashes.
Say I want the model to confirm commitment to a (secret) negotiation max price ceiling. The model need to list this in advance but I don't want to see it in advance. Since it is a number, a foreign language won't always be very efficient. So I ask the model to output only the hash. It then stores the real data in the (invisible) python call used to gen. the hash.
I wrote more about my approach to it here and here (I talk about "co-writing" but the difference between that and role-playing can be pretty slight). (I say that I like Claude Opus the most in those posts, but I switched to Sonnet 4.5 after it came out.)
That's a neat trick with the hashes! I haven't done adversarial role-playing with LLMs myself (the characters may be opposed but the writers are generally aligned), but I'll keep that in mind if I ever do.
Thanks for the links! I had seen part 1 but not 2 and I found part 2 more useful for myself. In general people don't talk a lot about editing. Valuable to do so.
I highly recommend anyone reading trying adversarial roleplay. It can be surprisingly rewarding with a strong session.
Some things to keep in mind is that cognitive load reduces ontological sensing and initial prompting affects frame-coherence. So you want to commit to a frame upfront to keep things coherent and logical. Then start prepping with more detailed instructions. Another thing is to really force the model to work against you 200 %.
Using a famous historic example as baseline inspiration works wonders.
Finally, a tip in general for Claude Sonnet (also my go to now,) is to ask it to list its epistemic functions first as a baseline. Anthropic models have poor epistemic humility across the board and this can make in-session corrections difficult. I do this first in new session, when I need the model to be corrigible on short notice.
Hm, yes, I have actually been working on a setup that Claude could GM to share. It has extensive support files (JSON) for the LLM, but still need some work. Are you into sci fi? (Assuming yes but better check preferences.) I could let you beta test it :)
I find that k5 responses are easy to generate once you give the LLM more space to be creative in response.
Curated and popular this week
