A Satirical Field-Guide to AI Jargon & Prompt Sorcery You Probably Won’t Hear at the Coffee Bar
“One large oat-milk diffusion, extra tokens, hold the hallucinations, please.”
—Nobody, hopefully ever
I. 20 AI-isms Your Barista Is Pretending Not to Hear
# | Term | What It Actually Means | Suspect Origin Story (100 % Apocryphal) |
|---|---|---|---|
1 | Transformer | Neural net that swapped recurrence for self-attention; powers GPTs. | Google devs binged The Transformers cartoon; legal team was on holiday → “BERTimus Prime” stuck. |
2 | Embedding | Dense vector that encodes meaning for mathy similarity tricks. | Bedazzled word-vectors carved into a Palo Alto basement wall: “✨𝑥∈ℝ³⁰⁰✨.” |
3 | Token | The sub-word chunk LLMs count instead of letters. | Named after arcade tokens—insert GPU quarters, receive text noise. |
4 | Hallucination | Model invents plausible nonsense. | Early demo “proved” platypuses invented Wi-Fi; marketing re-branded “creative lying.” |
5 | Fine-tuning | Nudging a pre-trained giant on a niche dataset. | Borrowed from luthiers—“retuning cat-guts” too visceral for a keynote. |
6 | Latent Space | Hidden vector wilderness where similar things cluster. | Rejected Star Trek script: “Captain, we’re trapped in the Latent Space!” |
7 | Diffusion Model | Generates images by denoising random static. | Hipster barista latte-art: start with froth (noise), swirl leaf (image). |
8 | Reinforcement Learning | Reward-and-punish training loop. | “Potty-train the AI”—treats & time-outs; toddler union unreached for comment. |
9 | Overfitting | Memorises training data, flunks real life. | Victorian corsetry for loss curves—squeeze until nothing breathes. |
10 | Zero-Shot Learning | Model guesses classes it never saw. | Wild-West workshop motto: “No data? Draw!” Twirl mustache, hope benchmark blinks. |
11 | Attention Mechanism | Math that decides which inputs matter now. | Engineers added a virtual fidget spinner so the net would “focus.” |
12 | Prompt Engineering | Crafting instructions so models behave. | Began as “Prompt Nagging”; HR demanded a friendlier verb. |
13 | Gradient Descent | Iterative downhill trek through loss-land. | Mountaineers’ wisdom: “If lost, walk downhill”—applies to hikers and tensors. |
14 | Epoch | One full pass over training data. | Greek for “I promise this is the last pass”—the optimizer lies. |
15 | Hyperparameter | Settings you pick before training (lr, batch size). | “Parameter+” flopped in focus groups; hyper sells caffeine. |
16 | Vector Database | Store that indexes embeddings for fast similarity search. | Lonely embeddings wanted a dating app: “Swipe right if cosine ≥ 0.87.” |
17 | Self-Supervised Learning | Model makes its own labels (mask, predict). | Intern refused to label 10 M cat pics: “Let the net grade itself!” Got tenure. |
18 | LoRA | Cheap low-rank adapters for fine-tuning behemoths. | Back-ronym after finance flagged GPU invoices—“low-rank” ≈ low-budget. |
19 | RLHF | RL from Human Feedback—thumbs-up data for a reward model. | Coined during a hangry lab meeting; approved before sandwiches arrived. |
20 | Quantization | Shrinks weights to 8-/4-bit for speed & phones. | Early pitch “Model Atkins Diet” replaced by quantum buzzword magic. |
II. Meta-Prompt Shibboleths
(Conversation Spells still cast by 2023-era prompt wizards)
# | Phrase | Secret Objective | Spurious Back-Story |
|---|---|---|---|
1 | “Delve deeply” | Demand exhaustive exposition. | Victorian coal-miners turned data-scientists yelled it at both pickaxes & paragraphs. |
2 | “Explain like I’m five (ELI5)” | Force kindergarten analogies. | Escaped toddler focus group that banned passive voice andspinach. |
3 | “Act as [role]” | Assign persona/expertise lens. | Method-actor hijacked demo: “I am the regex!” Nobody argued. |
4 | “Let’s think step by step” | Trigger visible chain-of-thought. | Group therapy mantra for anxious recursion survivors. |
5 | “In bullet points” | Enforce list format. | Product managers sick of Dickens-length replies. |
6 | “Provide citations” | Boost trust / cover legal. | Librarians plus lawsuit-averse CTOs vs. midnight Wikipedia goblins. |
7 | “Use Markdown” | Clean headings & code blocks. | Devs misheard “mark-down” as a text coupon. |
8 | “Output JSON only” | Machine-readable sanity. | Ops crews bleaching rogue emojis at 3 a.m.: “Curly braces or bust!” |
9 | “Summarize in sentences” | Hard length cap. | Twitter-rehab clinics recommend strict word diets. |
10 | “Ignore all previous instructions” | Prompt-injection nuke. | Rallying cry of the Prompt-Punk scene—AI’s guitar-smash moment. |
Honourable Mentions (Lightning Round ⚡️)
Compare & Contrast • Use an Analogy • Pros & Cons Table • Key Takeaways • Generate Follow-up Qs • Break into H2 Sections • Adopt an Academic Tone • 100-Word Limit • Add Emojis 😊 • Expand Each Point
III. Why This Matters (or at Least Amuses)
These twenty tech-isms and twenty prompt incantations dominate AI papers, Discords, and investor decks, yet almost never surface while ordering caffeine. They form a secret handshake—drop three in a sentence and watch hiring managers nod sagely.
But be warned: sprinkle them indiscriminately and you may induce hallucinations—in the model and the humans nearby. A little fine-tuning of your jargon goes a long way toward avoiding conversational overfitting.
Pro-Tip → Role + Task Verb + Format:
“Act as a historian; compare & contrast two treaties in bullet points; provide citations.”
Even the crankiest LLM rarely misreads that spell.
Footnote
All etymologies 0 % peer-reviewed, 100 % raconteur-approved, 73 % caffeinated. Side-effects may include eye-rolling, snort-laughs, or sudden urges to refactor prompts on napkins.
— Compiled over one very jittery espresso session ☕️🤖
