Hello team, today we are going to talk about another AI jargon word: prompts. A prompt is nothing more than the instructions you give to an LLM. There are different types of prompts.
- System prompts these are developed by companies like OpenAI and Anthropic to provide the super-set of instructions which guide model behavior at a foundational level.
- User prompts: These are your run of the mill chats, you know, when you confess your inner-most-thoughts to ChatGPT, Claude or Gemini; completely unaware that said thoughts may be publicly accessible. And while this is partially a joke, it has happened.
To the matter at hand. The first Prompt Engineering jobs appeared in 2023. While that was only three years ago, AI years move fast—closer to dog years. By 2024 and 2025, the once-hyped six-figure role was no longer six-figure, and in many cases, no longer a role at all. Still, you can find active Prompt Engineering job postings out there.
Prompt engineering is defined as “writing, refining, and optimizing inputs to LLMs and other generative models so they produce useful, task-aligned, and reliable outputs,” as noted by sources like IBM and AWS.
In this article, we are going to break down the rise (and quiet decline) of Prompt Engineering as a job title, what remains valuable in the skill, and why the future belongs to AI Prompt Frameworks. These frameworks are the evolution of prompt engineering—what happens when prompting becomes a real engineering discipline instead of a clever wording exercise.
This shift may redefine how teams build with AI, and if you learn to master frameworks, your results will feel almost unfairly good.
Are prompt engineering jobs a fad?
The short answer: yes—as an actual occupation people will pay-you-money for.
However if we widen our gaze, it gets more interesting.
Evidence from 2024–2025 shows that the Prompt Engineer role spiked early and then steadily faded, even as AI adoption accelerated. Multiple analyses show that job postings for dedicated prompt engineers are now a tiny fraction of AI-related roles.
As SalesforceBen noted, “Prompt Engineering jobs are becoming obsolete in 2025,” and the work is being folded into broader, more technical roles.
This LinkedIn analysis observed that employers now prefer candidates who combine prompting expertise with automation, AI integration, and workflow design. Even the Wall Street Journal reported the role as “already obsolete,” according to a widely shared summary.
This decline does not mean prompting is irrelevant. Quite the opposite indeed.
Prompting has become so fundamental that it is now a baseline expectation—more like knowing how to Google effectively than an actual specialization. Job titles such as AI Engineer, ML Engineer, Software Engineer (AI/Agents), Generative AI Engineer, and AI Product Engineer now list prompt engineering as a single bullet point within a broader set of responsibilities.
Prompting as a skill will persist. Prompt Engineering as a niche job title likely will not.
Which roles will require prompt engineering?
Prompt engineering is becoming a universal literacy across several AI-based roles.
Some of the roles incorporating prompt engineering as a skill are
1. AI engineers
AI engineers design, integrate, and optimize systems that use LLMs. They use prompt engineering to structure model behavior, constrain outputs, establish formatting rules, and ensure reliability across workflows.
2. ML engineers
ML engineers work on model tuning, RAG pipelines, fine-tuning, data preprocessing, and evaluation—each requiring prompts as structured, testable components.
3. Software engineers working with agents or AI APIs
Modern engineering increasingly involves integrating models with tools, memory, and structured tasks. Engineers must design prompts as part of agent reasoning loops, tool invocation instructions, and workflow orchestration.
4. Data engineers and analytics engineers
When a workflow involves classification, summarization, extraction, or quality verification, prompt engineering becomes a key part of the pipeline.
5. Product managers
Many PMs already use prompting for user story generation, research synthesis, competitive analysis, and prototyping. PMs often specify prompts used in product features.
6. Technical writers and documentation teams
A well-structured prompt is a form of documentation. Writers increasingly shape knowledge bases, examples, and templates used to guide models.
7. Designers (UX, Interaction, Conversational)
When interfaces include chat, guided flows, or AI-powered content, designers influence prompt structure, tone, and failure handling.
The key is this:
Prompt engineering is no longer a job. It is becoming an expected competency—like working with APIs or writing SQL.
Forget prompt engineering, learn AI prompt frameworks instead
This is where things get interesting.
Prompt Engineering as a job title may be fading, but prompt engineering as an problem solving skill is taking-off. Modern AI development has shifted from one-off clever prompts to structured prompting using reusable bundles—sets of instructions, templates, and workflows.
Think about it, instead of writing a brand-new prompt every time, you use a set of structured patterns that consistently produces better results.
The LlamaIndex documentation describes prompts as “first-class, testable artifacts,” and the LangChain blog highlights how frameworks help organize thinking into clear steps.—They are both programming tools.
This is why modern prompting may feel like cheating when done well.
When you use frameworks, you gain:
- Clear templates that reduce guesswork.
- A consistent way to think through problems step-by-step.
- Better reasoning from the model because you’ve given it structure.
- A way to reuse what works instead of starting from scratch.
- More predictable outcomes, even for complex tasks.
You don’t need to be an engineer to use AI Prompt Frameworks. Anyone—from product managers to designers to business analysts—can benefit from them.
This is the evolution of prompt engineering. And learning this will dramatically elevate the quality of your results.
Below is a breakdown of seven widely used frameworks the community has gravitated toward. These aren’t libraries—they’re mental models and structured approaches that you can apply to any LLM.
1. The ACE framework
ACE stands for Action, Context, Expectation.
This framework ensures the model knows:
- Action: What the model should do.
- Context: What information matters.
- Expectation: What the output should look like.
⠀ ACE is simple but powerful—great for precise tasks.
Use Cases:
- Converting messy text into structured data
- Generating summaries with a required format
- Preparing documentation or API explanations
2. The AIM framework
AIM stands for Aim, Information, Method.
It focuses on problem solving and controlled reasoning.
- Aim: What is the objective?
- Information: What inputs does the model have?
- Method: How should the model approach the solution?
Use Cases:
- Technical troubleshooting
- Data cleaning workflows
- Multi-step problem decomposition
3. The CARE framework
CARE stands for Context, Action, Requirements, Examples.
It is often used in high-stakes or reliability-sensitive tasks.
Use Cases:
- Compliance and policy alignment
- Safety-critical summarization
- Producing output templates with strict formatting
4. The CLEAR framework
CLEAR stands for Context, Logic, Expectations, Actions, Results.
It is designed for transparent reasoning and traceability.
Use Cases:
- Analytical reports
- Complex decision-making scenarios
- Evaluating model outputs against criteria
5. The ERA framework
ERA stands for Expertise, Role, Action.
This framework uses role specification to control tone, constraints, and depth.
Use Cases:
- Asking the model to speak as a domain expert
- Adjusting for beginners vs advanced readers
- Rapid prototyping of micro-consulting tasks
6. The IDEA framework
IDEA stands for Intent, Details, Examples, Action.
It is excellent for creativity, brainstorming, and content transformation.
Use Cases:
- Product ideation
- UX writing
- Marketing concept generation
7. The TRACE framework
TRACE stands for Task, Requirements, Actions, Checks, Execution.
This is one of the best frameworks for technical work, and its structure aligns with real engineering workflows.
- Task: What we’re trying to do
- Requirements: What constraints exist
- Actions: What steps the model should follow
- Checks: How to self-evaluate
- Execution: Final answer after validation
ERA Framework Example (Giddy Version)
Scenario:
You want the AI to help you come up with a delightful, slightly chaotic birthday plan for a friend who “doesn’t want a party” but absolutely does want a party.
E — Expertise
You tell the AI what kind of expert it should embody.
“You are a world-class celebration architect with deep expertise in creating joyful, mildly unhinged surprise experiences that people remember forever.”
R — Role
You assign the tone, behavior, and constraints.
“Your role is to design a birthday plan that feels spontaneous, playful, and just on the edge of too much — but never actually crosses the line. Think giddy, not reckless.”
A — Action
You tell the AI exactly what you want it to produce.
“Give me a 3-part birthday plan that:
- Starts calm
- Escalates into delightful absurdity
- Ends wholesome Also include one optional twist that will make everyone burst out laughing.”
I will not run this one for you, have fun feeding this to ChatGPT. Claude or Gemini.
A final thought
Prompt Engineering jobs may fade, but prompting as a skill will not. Like command-line fluency or version control, it becomes part of your baseline engineering literacy.
The future belongs to those who learn framework-based prompting—prompting as a discipline, prompting as structured reasoning, prompting as reusable systems.
If you want to stay ahead, learn the frameworks. Use them. Experiment with them. Build your own.
The gap between a casual AI user and a framework-driven AI one is enormous. And the moment you start applying these AI Prompt Frameworks, your results will improve so dramatically that it may feel like cheating.
And perhaps it is. But in the best possible way.
Good artists copy, great artists steal. — Picasso, Pablo
I invite you to steal these AI Prompting Frameworks, make them uniquely yours and you will become unstoppable.