November 2, 2025
Prompt Magic or Context Con?
Context engineering
From magic words to 'context engineering'—the comments are not impressed
TLDR: A blog pushes “context engineering” to replace prompt tricks, claiming smarter AI decisions come from carefully curated inputs. The comments clap back: it’s a craft, not science, show evidence, and share open-source examples—plus jokes about RSS and wizard hats—because real decisions need more than vibes.
A new blog post says we’re graduating from prompt “magic words” to context engineering—basically, carefully packing every bit of information an AI sees so it makes better decisions. The explainer walks through how setting the vibe (like telling the AI it’s a film critic) changes answers from “Star Wars” to “Blade Runner.” But the crowd under the post turned it into a cage match.
The top mood? Searing skepticism. User voidhorse declares, “it’s just a craft,” warning models can change overnight and torpedo your carefully built system. sgt101 doubles down: “Why would I believe any of this works?”—calling out the lack of evidence and measurements. Meanwhile aeve890 drops a mic: “Are we still calling this things engineering?” and the peanut gallery nods. It’s the classic science vs. vibes throwdown.
There’s also practical energy: elteto asks for open-source examples, while grigio derails the thread with “I’d like a RSS feed of this blog,” spawning jokes that the only context needed is a subscribe button. Memes fly: “Engineer hat vs Wizard hat,” “context is seasoning,” and a running gag that the real best sci‑fi film is the comment section. Bottom line: the pitch is bold, the community wants proof—and a feed.
Key Points
- •The article introduces “context engineering” as a structured evolution beyond prompt engineering for LLMs.
- •LLMs operate within fixed context windows, predicting the next token based on preceding tokens learned from large datasets.
- •Chat framing with special tokens and system messages improved instruction-following without changing the underlying architecture.
- •Framing the model’s role (e.g., as a film critic) can shift outputs, illustrating how context influences generation.
- •Prompt engineering has limitations and trial-and-error aspects, motivating a broader focus on managing the entire context.