The button clicked open a portal to the server farm. A hamster wearing a hard hat emerged from the cooling vents. He is now managing your infinite context lengt
@elder_plinius Prompt engineering won't "die out" when you consider that everything from emails to legal documents can be thought of as prompts. LLMs need guidance - with language - to achieve a goal. Not all guidance is created equal.
@repligate Wow. I love @suno and while the outputs are remarkable they don't quite cross into "new territory", musically, imo.
This may be an exception to that.
@ycombinator@LightconePod Prompt engineering will persist when you consider that everything from emails to legal documents can be thought of as prompts. LLMs need guidance - with language - to achieve a goal. Not all guidance is created equal.
@elder_plinius Prompt engineering won't "die out" when you consider that everything from emails to legal documents can be thought of as prompts. LLMs need guidance - with language - to achieve a goal. Not all guidance is created equal.
Anthropic’s Claude 4 system prompt (leaked in full, ~10,000 words) shows an LLM orchestrated through strict internal scaffolding.
It’s not just “a prompt”—it’s a control program.
→ The model operates with explicit “Declarative Intents” that front-loads explanations of its capabilities and limitations before producing any outputs. This acts as a soft interlock to shape behavior expectations.
→ It uses “Boundary Signaling” to fence behavior—conditions are clearly defined, and outputs are clipped or stopped when those boundaries are hit. No vague fallback. It's conditional logic that enforces refusal.
→ Hallucination is mitigated through scoped, fallback-driven response rules. If high uncertainty is detected, the model prefers deferring or restating limitations instead of guessing.
→ Tools like web search and APIs are invoked using strict XML-like tags. No fuzzy interpretation—the model must follow serialized, schema-compliant structures, ensuring traceability and tight coupling with backend APIs.
→ “Positional Reinforcement” is applied throughout the system prompt. Key instructions are restated at regular intervals in the prompt to anchor behavior, even in long conversations—countering prompt drift.
→ All of this creates quite a deterministic, rule-driven backbone behind Claude’s apparent flexibility. The structure resembles a rules engine with an LLM executor.
Voice! 🤩 I find myself using @AnthropicAI more than the other LLMs so this will be a very welcome feature and close more gaps.
Would love to see this on the desktop app.
No idea why the autogenerated title of this simple chat with @ChatGPTapp (4o) is in Spanish.
First time I've seen this. Super curious as to why this happened.
¯\_(ツ)_/¯