Why the Age of Prompt Engineering Is Over

Why the Age of Prompt Engineering Is Over

From “how you say it” to “what you show” — the game has changed.

Prompt Engineering as a Profession

In 2023, a new profession appeared.

Prompt engineer.

“Think step by step.” “You are an expert with 20 years of experience.” “Let me show you some examples first.”

Sentences like these became know-how worth tens of thousands of dollars. The same question would produce dramatically different answers from AI depending on how you phrased it.

Prompt engineering genuinely worked. A single Chain-of-Thought line raised math scores by 20%. A single sentence assigning a role changed the depth of expertise. Three few-shot examples gave complete control over output format.

This was not hype. It was real. So why is it ending?

Why It Worked: Because the Models Were Dumb Enough

Look at why prompt engineering worked from first principles. It is simple.

Early LLMs were poor at grasping user intent. Say “summarize” and they would rewrite instead. Say “compare” and they would list instead.

Because the model misread intent, the skill of conveying intent precisely became valuable. Prompt engineering was essentially “interpretation” — translating human intent into a form the LLM could understand.

For interpretation to be valuable, there must be a language barrier.

What Changed: The Models Got Smart

From GPT-3.5 to GPT-4. From Claude 2 to Claude 3.5. With each generation, the models’ ability to grasp intent improved dramatically.

Say “summarize” and they summarize. Say “compare” and they compare. Even without being told to “think step by step,” they break complex problems into steps on their own.

The language barrier got lower. The value of interpretation shrank.

Prompt techniques that produced dramatic differences in 2023 produce only marginal differences in 2025. When the model is smart enough, phrasing matters less and less.

So what matters instead?

The Context Window: A Law of Physics

LLMs have one physical constraint.

The context window.

Whether 128K tokens or 1M tokens, it is finite. Only information that fits inside this finite space influences reasoning. Information outside the window, no matter how important, might as well not exist.

This is independent of model size. Even with a trillion parameters, the context window is finite. Even with training data spanning the entire internet, the context window is finite.

No matter how smart the model is, if wrong information enters the context, it produces wrong answers. If irrelevant information fills the context, it misses what matters. If needed information is missing from the context, it is as good as unknown.

Prompt engineering was a problem of “how you say it.” The new game is a problem of “what you show.”

This is context engineering.

Analogy: The Open-Book Exam

Here is an analogy for the difference between prompt engineering and context engineering.

Prompt engineering is writing the exam questions well. Instead of “choose the correct answer below,” write “derive the answer step by step that satisfies all of the following conditions” — and the student gives a better answer.

Context engineering is the question of which books you bring to an open-book exam. No matter how well the exam questions are written, if the student brought the wrong books, they cannot answer. The number of books you can bring is limited. Which books you bring determines your grade.

When the model was dumb, the question format (prompt) mattered. When the model is smart, the reference material (context) matters.

The Agent Era Accelerates the Shift

This shift is accelerating with the emergence of agents.

Prompt engineering is written by humans every time. Humans write the question, humans explain the context, humans specify the format.

Agents are different. Agents reason on their own, call tools, and collaborate with other agents. At each step, they must compose the context themselves.

An agent called an external API and received data. This data needs to go into the context for the next round of reasoning. Which parts go in and which parts get left out? Which previous reasoning results are kept and which are discarded? Can information sent by another agent be trusted?

A human cannot make all these decisions every time. For agents to operate autonomously, context composition must be automated.

Prompt engineering was a human skill. Context engineering must be a system capability.

Prompt Engineering Is Not Disappearing

Let me prevent a misunderstanding.

I am not saying prompt engineering is becoming meaningless. System prompts are still important. Output format specification is still necessary. Declaring roles and constraints is still effective.

What is shrinking is the share that prompt engineering holds.

If 70% of output quality was determined by the prompt in 2023, in 2025, 30% is determined by the prompt and 70% by the context.

The ratio has flipped.

And this trend is not reversing. Models will keep getting smarter, and the smarter they get, the less phrasing matters and the more context matters.

But Context Engineering Has No Infrastructure

Here is the crux.

Prompt engineering had tools. Prompt templates, prompt libraries, prompt testing frameworks. An entire ecosystem for systematically managing “how you say it” was built.

Context engineering does not have this yet.

Look at how context is handled in practice right now.

RAG pipeline chunk sizes are tuned by hand. Background information is written into system prompts by hand. What to store in an agent’s memory is designed by hand. Which search results to put into the context is decided by hand.

All of it is manual.

And the raw material for all that manual work is natural language. Natural language documents are cut up in natural language and pasted into a natural language context.

Natural language has low information density. No sources. No confidence levels. No timestamps. Unnecessary tokens are consumed to convey the same meaning. There is no way to automate quality judgment.

This resembles the pre-prompt-engineering era. Prompt engineering was also manual at first. It relied on individual intuition and experience. Then tools and methodologies emerged and it became systematized.

Context engineering is at that prior stage right now. The problem has been recognized, but the infrastructure does not exist.

What the Infrastructure Needs

For context engineering to move from manual work to a system, at minimum the following are required.

Compression. A way to fit more meaning into the same window. Strip away natural language’s grammatical glue and leave only meaning, and the effective window size multiplies — without changing the model.

Indexing. A way to find the right information precisely. Search based on semantic structure, not embedding similarity. A search where looking for “Apple revenue” does not pull up “apple nutrition facts.”

Validation. A way to mechanically reject information that does not meet spec. Just as a Go compiler catches unused variables as errors, claims without sources and facts without timestamps should be filtered out before entering the context. The cheapest and most deterministic checks must come first.

Filtering. A way to judge semantic quality. If validation looks at form, filtering looks at content. Relevance, reliability, freshness. Is this information truly needed for this round of reasoning?

Consistency. A way to guarantee the internal coherence of the selected information set. Individually good pieces of information can contradict each other when combined. If the CEO from 2020 and the CEO from 2024 both enter the context simultaneously, the LLM gets confused.

Composition. A way to optimize placement and structure within the window. The same information receives different attention weights depending on where it is placed. Front or back? How is it grouped?

Accumulation. A way for the system to learn and grow over time. Caching is the reuse of individual results. Accumulation is learning which context compositions produced good results, and growing the knowledge base itself.

These seven are the full stack of context engineering infrastructure.

This Is Not About Any Particular Tool

Let me be frank.

Who builds this infrastructure is an open question. One tool might solve everything, or multiple tools might each handle a layer.

But the fact that infrastructure is needed is not an open question.

That the context window is finite is a physical fact. Even if the window grows 10x, the world’s information grows faster. That natural language has low information density is a structural fact. That agents need automated context management to operate autonomously is a logical necessity.

Just as prompt engineering needed tools, context engineering needs tools. But this time, the nature of the tools is different.

Prompt engineering tools were closer to text editors. Context engineering tools are closer to compilers.

Compress information, index it, validate it, filter it, check consistency, optimize placement, and accumulate results. This is not editing. This is engineering.

That is why it is called context “engineering.”

Summary

Prompt engineering was valuable when models were dumb. Because models could not read intent, the skill of conveying intent well mattered.

As models got smarter, the game changed. From “how you say it” to “what you show.” From prompt to context.

The emergence of agents accelerates this shift. Humans cannot assemble context every time. The system must do it on its own.

But right now, context engineering has no infrastructure. Natural language is being cut and pasted by hand.

The required infrastructure has seven layers: compression, indexing, validation, filtering, consistency, composition, accumulation.

It is not the age of prompt engineering that is ending. It is the age when prompt engineering alone was enough.