@Anitahityou: The prompt engineering still being touted in '24 is already dead. Today's LLM is an intent reconstructor. Clarity is important, but richness is more important. Because human real thinking is not linear; it is jumpy, chaotic, emotional. An over-compressed prompt can...
Summary
This article argues that traditional prompt engineering is obsolete; modern LLMs are intent reconstructors, and interactions should be through natural, rich conversation rather than condensed instructions.
View Cached Full Text
Cached at: 06/23/26, 11:48 AM
The prompt engineering still hyped in 2024 is already dead today.
Today’s LLM is an intent reframer. Clarity matters, but richness matters more.
Because real human thought is not linear—it’s jumpy, messy, and emotional.
An overly compressed prompt may be clean, but it loses nuance. Vocal rambling, on the other hand, can preserve more of the true intent.
So the best way to interact with AI is not by issuing commands, but by confiding in a friend who is extremely smart but needs plenty of context.
What you feed it is not nonsense—it’s intent density. Forget prompts, and just start talking.
Phoenix Yin (@Phoenixyin13): Modern LLMs are essentially intent simulators trained on vast amounts of human language data. The closer your language is to the real human thought process, the easier it is for the model to match the rich patterns it learned during training.
Early prompt engineering was like writing API call instructions for a clunky tool. Now, it’s more like co-creating with a super-intelligent conversation partner. You don’t need perfect expression—you just need to speak honestly.
Similar Articles
@LiuVaayne: This is a long article by yan5xu (former ManusAI): "From Prompt to Harness: How to Understand LLM Engineering" Core Framework: The Spiral Evolution of Engineering Paradigms First Loop: Prompt Engineering (2022-2024) ...
This article by yan5xu (former ManusAI) proposes a spiral evolution model for LLM engineering paradigms: from Prompt Engineering (2022-2024) to Context Engineering (2025), then to Harness Engineering (2026-), and discusses the bottlenecks and driving factors at each stage.
Your LLM Doesn’t Need Better Prompts — It Needs an Agent Harness
An article discusses the need for Agent Harness Engineering—structured systems with tool validation, context management, guardrails, telemetry, and verification loops—to make LLM agents reliable in production, arguing that better prompts alone are insufficient.
@yan5xu: https://x.com/yan5xu/status/2059117572826746979
The article discusses three stages of LLM engineering evolution from Prompt Engineering to Harness Engineering, reflecting the progression of AI engineering practices.
Humanising LLM Outputs Is Dumb
An opinion piece arguing that humanising LLM outputs via prompt instructions is the wrong abstraction—agents should exchange high-fidelity data and only compress into human-friendly prose at the final boundary.
@jakevin7: Sharing a god-tier review prompt methodology. The LLM self-correction survey 'When Can LLMs Actually Correct Their Own Mistakes?' concludes that without reliable external feedback such as test results or tool outputs, a model relying only on self-reflection often cannot steadily correct errors...
Shared a prompt methodology based on LLM self-correction research, emphasizing that self-checking is limited without external feedback, and recommending progressively enhanced prompting strategies such as adversarial review.