this post was submitted on 17 Jan 2026
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The literal firat thing I did with my lightweight local LLM was describe specified scenarios to it and ask it to generate a prompt for itself that would make that 'profile' of it always have that context.
Can you explain that a little more in depth. I’ve been experimenting with local LLM and am curious what type of scenarios you’re talking about and how this affected your LLM output.
Ok, for starters, I'm using Alpaca, a flatpak than acts as a kind of simplified, containerized way of managing local LLMs, it has a few basic tools you can use with LLMs, manages downloading them, and then you can make profiles based off of the model you've dl'ed, with specific context prompts, tweak a few settings for them.
So far I am most fond of the Qwen3 model, but, ymmv.
more explanation encapsulated herein
Uh lets see, for things like getting up to date with a particular coding language's modern syntax revisions or updates to a particular library, something like that.
Feed them some webpages of the documentation, ask them to read them, or ask them to do some of their own self directed searching to find changes, then say hey, now please generate a contextual prompt for yourself that would summarize and inform you of key points/changes.
There are a decent number of fairly powerful, fairly lightweight models, but, they tend to be some months or a year or w/e out of date in their training data, so doing this acts as a kind of 'update' for them.
You can also do this with... some set of fairly niche topics that their lightweight model just isn't that accurate about, for maybe the purpose of... maybe brainstorming worldbuilding scenarios, giving it more recent scientific/news updates, or even asking it to try to roleplay as some specific fictional or real character.
Though I've not really tried the latter scenario beyond once as basically a gimmick.
Its not like, guaranteed to make them super intelligent experts, its more like... going through a crash course and giving them a bit of a more accurate generao overview.
Any situation where they ... keep making specific, minor, fairly simple mistakes... this kind of thing can be decent at at least triaging.
I've mostly been trying to craft a coding assistant out of this, and I've had pretty decent success at getting it to 'learn' sets of syntax updates, deprecated methods that now have very close equivalent replacements.
Another thing you can do with this kind of thing is... the adversarial approach. Give two different models the same intial set of 'hey here are some things i want you to know, now generate a prompt for yourself'. Then take the generated prompt, give it in conversation to the other LLM, ask it to evaluate it and compare it to the prompt it generated, etc.
The general downside to this is that with a lightweight model, on low power hardware, the longer/more complex the intial prompt they have is, well, i guess its kind of comparable to a game having to compile shaders before a first run... you can end up in situations where you've just given them so much 'context' that it just exceeds your local HW compute power to be able to... evaluate the prompt and then generate a first actual conversstional response.
Hah!
I am doing this on a Steam Deck, running Bazzite.
I keep it 'docked', powered up, when running an LLM on it... but yeah... anybody can do this.
Its really not that hard, you do not need a battle station.
A battle station would obviously help with more complex tasks.
But its uh ... not necessary.
I imagine the GabeCube would be even better at this.
At one point I sat down and worked out the math, and yeah, I could get a solar power station thingy, and... just actually be able to run the whole setup off of sunlight.
Its not instantaneous, it takes some time to generate complex answers/responses, but it does work.
So... yeah, I functionally could have a local LLM that is mostly kinda portable, if you carried a battery pack / dock on your person in a backpack with the Deck, or in your car, maybe along with said solar power station.
Well I recently gave it a... roughly 600 line script of GDScript from a Godot project thats roughly a yearish old... and asked it to evaluate it, and then refactor it, in line with 4.6 syntax and methods, and... in total, the actual refactoring took between 5 to 10 minutes, roughly.
Its... much faster with smaller snippets or chunks.
How fast is this compared to big players?
Well... with small snippets, an online free to use LLM of some kind is much faster.
But... they generally don't have ways that you can actually do the whole custom prompt thing that I described, not for free, infinite use. So you have to keep telling them about the same silly errors they'll make.
They're useful in generating the actual prompt for your local LLM, as they're ... you know, online, and can usually rapidly pull up relevant pages that go over API, syntax, method, feature changes, then reformulate all of them into a prompt-like format.
But... while free online LLMs may be faster... they tend to have hard limits on max tokens per day and/or on the size of input you can give them at once.
So... with that 600 line script, I would have had to feed chunks of that to it, then ask it to evaluate all of it, after first giving it the whole prompt of 'these are all the relevant syntax mistakes you are going to make if i dont tell you about them first'.
So in that kind of scenario, I'd say what I am doing with the local LLM is just actually net faster, more accurate/consistent, and requires less babying/manual input from me.
Oh and I don't have to pay anyone to keep using the premium version of my entirely local LLM.
And, other than Ecosia's LLM... the data center based big boys don't tend to be capable of running off of renewable energy, though I've no actual idea if Ecosia's actually make good on that claim.
So, I've been, in the last few hours, basically uh... distilling or refining the prompt.
Main goal was just to explictly capture more ... basically very specific rules that define like, this method is deprecated, use this one now, here is a generalized example, here's the old syntax, here's the new syntax, etc.
Keep running through scripts, keep asking it to do stuff, if it produces things with syntax errors, more or less work through why that is happening, rework or add to the prompt.
But something I did not expect to make a significant difference was to add a blurb to the top of the prompt that basically says 'be concise and non verbose unless asked to explain a concept or brainstorm a bigger picture, conceptual approach type question'.
So that resulted in it still being able to be conversational and contemplative, but when you're just feeding it code, errors, simple commands?
Well that makes it stop generating extraneous paragraphs telling you how wonderful your idea is and how happy it is to restate your request and tell your how its going to do it, etc etc.
Having it not do that actually significantly sped all this up, haha, as it... apparently devotes a lot of 'brain power' to figuring out how to write dumb fluff intros and outros.
And on a low power rig, its pretty impactful to strip out the bs.
But thats not an answer to your question lol.
I am just using the basic Qwen3 model, the 8B variant.
If you close Steam and don't have a browser with 10+ tabs open, this will work, it will run, hasn't blown up (yet).
I did have to uh, 6x the context size in the settings, that seems to be roughly optimal for ... not stalling out on actually reading the entirety of larger inputs, but also not blowing past the actual hardware limits.
So anyway, Qwen3 is a generalized model, and it actually just already understands GSScript circa roughly 4.1, seems to be when its training data set was formalized.
I started this all with something like 'tell me everything you know about modern GDScript for Godot' and it basically wrote a structured mini report, that served as an initial template to tweak update, revise.
So, my process has been:
Its now 2026, Godot 4.6 is out now (basically), this other online LLM said here are a bunch of things that have changed since 4.1, make a prompt that basically updates yourself.
As I tried to describe... Its basically a trial and error process of 'refining' or maybe 'distilling' the prompt, to get it to be comprehensive, but not too wordy, to focus it in without missing critical details.
Uh... i copy and past the code in, after either a simple command, or a question, or something like that, then do:
code
... and then return to comments or questions or directions if I have more.
Or maybe if the code is throwing either syntax or runtime errors, give it the errors.
Then, it generates some output, with a code block, I examine it, copy and paste it back to wherever, see if it throws syntax errors, see if it runs, see if it broke something, see if new thing I'm trying to make actually works right, etc.
much more rambling thoughts of someone who hopefully does not have a form of ai psychosis
I don't even know what Codex is.
I've been writing code for decades and I... I still don't like 'get' IDEs, most of the time? They almost always seem like more trouble than they are worth.
I'm just used to much more lightweight editors, or as with the case of Godot, it has a pretty decent code editor / manager window thingy.
This is all an excercise in ... optimizing laziness, lol.
But uh yeah, Alpaca is a kind of containerized way of handling LLMs.
The whole idea is that it is self contained, sandboxed.
I am extremely hesitant... to try and like, build my own version of Copilot, that is... insanely potentially dangerous.
So yeah, there are walls between the LLM and the rest of the system, thats the point.
I could try and like, build an automated workflow, but... it makes mistakes too often, and frankly, its... basically kinda like partner coding.
I point out mistakes it makes, it points out mistakes I make.
It is a communicative collaborative process, if you're doing anything remotely conceptually complicated.
And you can just tell the thing 'do a sanity check on this code' or ... describe an idea and ask it to critique it, or ask it to ask you questions that it might have.
I basically just treat it as a fellow programmer, you know, another autist, sort of like another normie human, sort of not, lol.
Often, it will kind of... conceptually pigeon hole itself into a particular way of trying to solve a problem.
We'll spend time trying to get this to work, it doesn't, I get frustrated, engage my own actual brain for a bit, realize what it is trying to do is fundamentally nonsensical, propose a different approach.
Sometimes, nope, my idea isnt compatible, it says as much.
Sometimes it basically has a facepalm moment and says wow thats a much simpler way to do this, and then we figure it out in like the next 10 minutes.
... This is very much like real coding with other real people, at least in my experience, lol.
Brains together strong, theoretically.
Oh! Ok, that makes more sense, yes, I've poked around with Cursor... at least once, at some point?
But yeah, I'll also very often ask the LLM to... draw up some kind of plan, before it makes some larger scale modification, or if we're trying to add something that would have to span across multiple scripts, etc.
You've also been coding longer than I have, hah!
I wasn't even alive for most of the 80s... but I do remember having to actually remember phone numbers, hahaha!
They used to have cords! You were fancy if you could take a phone from the kitchen to the couch, whoah, cordless!
But anyway, yeah I wouldn't have even considered trying this if it would have wound but being totally reliant on an internet connection, someone else's computer doing the actual work.
That, quite literally, is how they getcha.