A lighthearted archive of once-crucial prompt-engineering hacks made obsolete by modern LLMs.
Welcome to Boomer Prompts—an affectionate trip down memory lane of the
elaborate, quirky, and sometimes overkill techniques used to guide
earlier Large Language Models.
These “relics” showcase just how much LLMs have evolved—where once you
needed to triple your instructions and role-play as a wise oracle,
now simpler, more direct prompts suffice. Read on for a chuckle, and
discover how far we’ve come!
Chain of Thought Prompting
Instructing older LLMs to reason step-by-step out loud.
Why It Was Relevant: Early LLMs often needed
explicit “thinking aloud” instructions to handle logical leaps.
Why It’s Obsolete: Today’s instruction-tuned
models can process complex reasoning without heavy scaffolding.
Models That Still Benefit: Legacy GPT-2/GPT-3
endpoints with shallow context windows and minimal fine-tuning.
"Telling a modern LLM to think step-by-step is like
reminding a chess grandmaster how to move pawns."
In the past, prompts like “First, do X. Then Y. Finally Z” guided
older models around logical pitfalls. Now advanced LLMs typically
handle this internally, making forced, explicit reasoning a relic
of the past (though still occasionally useful in unique debugging
scenarios).
Few-Shot Overkill
Bombarding older models with lengthy examples to ensure they “get it.”
Why It Was Relevant: GPT-3.0-era models often
needed multiple demonstration examples to produce consistent results.
Why It’s Obsolete: Modern instruction-tuned LLMs
can handle tasks with minimal or zero-shot instructions.
Models That Still Benefit: Smaller open-source
or non-instruction-tuned models that rely heavily on pattern matching.
"One example is cool, but 12 repetitive examples used to be safer,
just in case!"
Early prompting strategies had users stuffing multiple sample inputs
and outputs to force the right pattern recognition. While helpful for
older models, new systems usually handle the gist with just a single
example or none at all.
Exaggerated System Prompt Stacking
Repeating critical instructions multiple times to avoid being ignored.
Why It Was Relevant: Early engines sometimes
dropped or overrode your instructions unless hammered in repeatedly.
Why It’s Obsolete: Current models follow
well-structured prompts consistently—less is more.
Models That Still Benefit: Older LLaMA or
basic expansions lacking robust instruction following.
"Like shouting 'OBEY!' at a dog that’s already sitting…
We can chill now."
System prompt stacking was used to brute-force compliance from
less capable models. Today, such repetition can confuse advanced
LLMs or needlessly consume tokens. A single well-crafted system
directive usually suffices.
Overly Verbose Role-Playing
Spinning elaborate persona backstories (“You are a wise oracle…”) just for tone.
Why It Was Relevant: Older models needed
extended persona details to maintain consistent style or voice.
Why It’s Obsolete: Modern LLMs adopt style
from concise, direct instructions; no multi-paragraph fluff needed.
Models That Still Benefit: Niche open-source
models with limited training on style or role-play data.
"Even if you don’t ask me to be your kindly grandma,
I still know how to keep the right tone."
Extensive persona prompts once drove coherence and style, especially
for smaller models. Today’s advanced LLMs quickly adapt to your
desired voice with just a sentence or two, so the multi-paragraph
persona is mostly comedic overkill.
Excessive “Stay on Topic” Warnings
Reminding older models with each prompt to remain consistent and not stray.
Why It Was Relevant: Less stable or smaller
models could drift drastically after a few messages.
Why It’s Obsolete: Modern LLMs maintain style
and tone from a single directive; repeated warnings waste tokens.
Models That Still Benefit: Minimal
fine-tuned clones with shaky context alignment.
"They were basically spam reminders. Now the model just goes
'Yep, got it' and continues on track."
Early or small-scale models lacked robust instruction anchors,
so “keep it consistent” warnings were frequent. With stronger
instruction tuning, these constant nudges are borderline pointless.
Triple Instruction Repetition
Issuing the same directive thrice to ensure compliance.
Why It Was Relevant: Some older models
parsed instructions poorly, performing better after repeated phrasing.
Why It’s Obsolete: Modern LLMs parse instructions
in a single pass, making repetition a waste of tokens.
Models That Still Benefit: Early GPT-3 or
small community models with minimal instruct tuning.
"I said do it. Do it. Do it. Good times…"
Repetition was a crude hack to ensure older models fully grasped
your instructions. Now, advanced models rarely need such heavy-handed
techniques: say it once, say it right.
Manual Summaries
Hard-coding repeated summaries of conversation context every few turns.
Why It Was Relevant: Older or smaller models
couldn’t hold context well, so manually summarized past content
helped them “remember.”
Why It’s Obsolete: Larger context windows and
improved memory in current LLMs make frequent manual summaries
less critical.
Models That Still Benefit: Low-parameter
open-source models with tight context limits.
"Like leaving sticky notes around for a forgetful roommate."
For older engines that lost context easily, summarizing the
conversation was essential. Now, improved attention mechanisms
let advanced models track lengthy dialogues without so much hand-holding.
Quick Quiz: Which Technique Is Still Somewhat Useful?