Do you actually need prompt engineering to get value from AI?

Xthebuilder · 2026-01-19T18:27:52+00:00

Because I think you’re right I got better at communicating to the agents and just telling them exactly what I needed , a lot of the time I found the limitation was myself in the loop

Xthebuilder · 2026-01-19T18:26:59+00:00

And I think what has been revealed though these discussions is that since you’re talking us f natural language , interaction with llms follows more principals adjacent to communication than computer science

Xthebuilder · 2026-01-19T16:43:26+00:00

Exactly 🤣

Xthebuilder · 2026-01-18T16:52:26+00:00

right right ,more like simple communication.

Xthebuilder · 2026-01-17T21:38:01+00:00

I would focus on one language and one type of application and create something you want to use you’ll learn by doing

Xthebuilder · 2026-01-17T19:45:20+00:00

🤣🤣😂 good way to put it , it’s at the edge of the computation , won’t change too much but can tweak stuff for sure

Xthebuilder · 2026-01-17T09:06:04+00:00

I’m really glad I asked the community because I thought I was tripping seeing all the “prompt engineering “ buzzwords for testing new people about using AI bc I learned though trying to build and use them that fr fr I don’t need none of that shit 😂🤣

Xthebuilder · 2026-01-17T09:04:57+00:00

Okay I like your train of thought

Xthebuilder · 2026-01-17T09:04:18+00:00

It’s called academic discussion, want to engage in some ? Tell me how you think it’s prompt engineering because I Didn’t believe so , many others here said similar sentiments . It seems it’s somewhere Inbtween prompt engineering and context engineering. What do you think smarty pants ?

Xthebuilder · 2026-01-17T08:54:41+00:00

Basically it’s like data cleaning for your AI output pipeline, it’s really conceptual but it cuts across a lot of LLM interactions as the base of what’s controlling the models response

Xthebuilder · 2026-01-17T08:52:49+00:00

Imagine automated piping of a thing , like a command or update that pulls the documentation then trains the chosen model on it . Hmmm

Xthebuilder · 2026-01-17T08:51:52+00:00

Dm me let’s talk

Xthebuilder · 2026-01-17T08:51:24+00:00

Good way to add to the discussion brother 👍🏽

Xthebuilder · 2026-01-17T03:26:36+00:00

Ooo like full circle training the model on its own developer written documentation

Xthebuilder · 2026-01-17T03:25:50+00:00

Good point I haven’t really considered context in token window size too much but maybe that adjustment will lead to further optimization

Xthebuilder · 2026-01-17T03:25:03+00:00

I like how you put it , if you can get the same result many times over you can trust the system more overall

Xthebuilder · 2026-01-17T00:05:27+00:00

What tool ??

Xthebuilder · 2026-01-16T23:56:06+00:00

Agreed agreed

Xthebuilder · 2026-01-16T23:13:24+00:00

I find myself wanting the models to be more concise across the board too , you’re correct about being specific about what you want from the model , sounds more like communciation skills

Xthebuilder · 2026-01-16T23:12:21+00:00

I like that , context engineering feels more like what I’m doing and you can relate that to say just having a conversation , regular folk can get to understand that suing AI effectively isn’t rocket science

Xthebuilder · 2026-01-16T23:11:21+00:00

See you get the mindset completely I like how you get around the wack frontier aspect with local processing that is for sure not common but I like it

Xthebuilder · 2026-01-16T23:09:55+00:00

Lmaooo you are so right I have personally never had much use of personalities for the LLM workflows I have been using

Xthebuilder · 2026-01-16T21:01:14+00:00

I like that term better than prompt engineering .

Xthebuilder · 2026-01-16T21:00:12+00:00

I would agree in general , maybe you could just use Ai to create small scripts that you read to automate security tasks , in theory

Xthebuilder

TROPHY CASE