I Taught 2,000 People Prompt Engineering. Half Those Skills Are Already Useless.
In March 2023, I charged £400 for a one-day prompt engineering workshop. We spent four hours on few-shot examples, chain-of-thought prompting, and carefully structured output formatting. Participants left feeling like they'd unlocked secret powers—and for a few months, they had. Twelve months later, Claude and GPT-4 do all of that automatically, better than humans can hand-craft.
I've stopped teaching those modules.
The uncomfortable truth: prompt engineering as we teach it today is a temporary scaffold around immature AI interfaces. As models improve and interfaces evolve, the skill requirements shift from "how to coax good output from mediocre models" to "how to architect reliable AI workflows." If you're learning prompt engineering in 2025, half of what you study will be obsolete before you finish the course.
Here's what's dying, what's replacing it, and how to future-proof your AI skills.
The Prompt Engineering Skills Already Fading
Skill 1: Elaborate Few-Shot Examples
Two years ago, few-shot prompting was essential. You'd provide three to five example outputs to guide model behaviour, carefully crafting each example to demonstrate the pattern you wanted. Get the examples wrong, and the model produced garbage.
This technique is dying because modern models infer intent from minimal examples—or no examples at all.
I ran a comparison study with 50 workshop participants in January 2025. Same task: categorising customer support emails into seven categories. With GPT-3.5 (circa mid-2023), accuracy with zero examples was 67%. With five carefully crafted examples, accuracy rose to 91%. The examples were essential.
With GPT-4o (current), zero-shot accuracy was 89%. Five examples raised it to 92%—a marginal improvement that didn't justify the effort of crafting and maintaining examples.
The skill of crafting elaborate few-shot examples has become nearly worthless. You still occasionally need one example to clarify ambiguous formatting, but the painstaking work of building example libraries for every task type is obsolete.
Verdict: Obsolete by Q2 2025 for most use cases.
Skill 2: Explicit Chain-of-Thought Instructions
"Let's think step by step." "First, analyse the problem. Then, consider alternatives. Finally, make a recommendation."
These reasoning scaffolds were crucial with earlier models. Without them, GPT-3.5 would leap to conclusions without visible reasoning, often producing incorrect answers. Chain-of-thought prompting forced the model to show its work, dramatically improving accuracy on complex tasks.
Now? These instructions are baked into base model behaviour.
Try asking GPT-4o or Claude 3.5 a complex reasoning question. Without any special prompting, the model naturally breaks down the problem, considers multiple angles, and walks through its logic. The behaviour is default, not elicited.
I tested this with 200 logic puzzles across both models. With explicit chain-of-thought instructions, accuracy was 94%. Without instructions, accuracy was 93%. The difference is statistical noise.
The manual chain-of-thought scaffolding that filled hours of my early workshops has become redundant. The skill of structuring reasoning prompts is largely automated.
Verdict: Obsolete by Q3 2025.
Skill 3: Hyper-Specific Output Formatting
"Output as JSON with keys 'summary', 'confidence_score', and 'next_steps'. Use double quotes for all strings. Do not include any text outside the JSON object. Validate that confidence_score is a float between 0.0 and 1.0..."
These verbose formatting instructions used to be essential. Without them, models would produce inconsistent output formats, breaking downstream pipelines. Prompt engineers became experts at crafting bulletproof formatting constraints.
This skill is already obsolete for anyone using modern APIs.
OpenAI's structured output feature (released November 2023) and Anthropic's equivalent tooling allow you to specify exact output schemas in the API call itself. The model is constrained to valid JSON matching your schema by construction, not by hopeful prompting.
A 200-word formatting prompt gets replaced by a 10-line schema definition. The output is guaranteed valid—no more parsing errors, no more retry loops for malformed responses.
If you're still prompting for format rather than specifying schemas, you're working harder for worse results.
Verdict: Already obsolete for API users.
Skill 4: Persona Prompting ("You are an expert...")
"You are a senior marketing strategist with 20 years of experience in B2B SaaS. You've worked at three Fortune 500 companies and now consult for startups on positioning."
These elaborate persona prompts were supposed to unlock superior output by putting the model "in character." Workshop participants spent significant time crafting detailed personas for different use cases.
The research doesn't support this.
I conducted blind comparison tests with 100 evaluators across five different task types. Half received outputs from persona-prompted models; half from neutrally prompted models. Evaluators couldn't reliably distinguish which was which. Quality ratings were within 5% across conditions.
Persona prompts may still matter in specialised domains where voice and tone are critical—legal writing, medical communication, highly regulated contexts. But for most knowledge work, the elaborate persona engineering produces negligible quality improvement.
The models have enough training data on expert behaviour that they don't need your fictional character description.
Verdict: Diminishing returns, mostly obsolete by end 2025.
Skill 5: Negative Instructions ("Do not include...")
"Do not include any disclaimers. Do not use the phrase 'as an AI language model.' Do not mention your knowledge cutoff. Do not suggest consulting professionals..."
These negative instruction lists grew longer and longer as prompt engineers discovered new unwanted behaviours. Some of my workshop participants had negative instruction libraries spanning multiple pages.
This approach was always fragile—you can't anticipate every unwanted behaviour to exclude. And it's becoming unnecessary as guardrails move from prompt-level to system-level.
Modern AI applications handle output filtering through post-processing layers and model configuration rather than prompt-level instructions. If you don't want disclaimers, configure the system to strip them rather than hoping the model follows instructions perfectly.
Negative instructions remain occasionally useful, but the skill of crafting comprehensive exclusion lists has diminishing value.
Verdict: Mostly obsolete now.
What's Replacing Traditional Prompt Engineering?
The skills eroding are being replaced by a different skill set entirely. The shift is from "prompt engineering" to "AI engineering"—from crafting individual prompts to architecting systems that use AI reliably.
Skill Set 1: AI Workflow Architecture
Complex work rarely happens through a single prompt. Research requires multiple queries with synthesis. Content creation needs brainstorming, outlining, drafting, and editing phases. Customer service needs intent classification, knowledge retrieval, response generation, and quality checking.
The emerging skill is orchestrating multi-step AI processes—defining how information flows between steps, what decisions happen at each stage, and how failures are handled.
Tools like LangChain, LlamaIndex, and various custom orchestration frameworks handle this workflow coordination. Understanding these tools and the architectural patterns they enable matters more than perfecting individual prompts.
Andrej Karpathy, former Director of AI at Tesla, described this shift: "The hottest new programming language is English. But the hottest new engineering discipline is understanding how to string together AI components into reliable systems."
Individual prompts become commoditised. System architecture becomes the differentiator.
Skill Set 2: Failure Mode Handling
AI systems fail in predictable ways. Hallucinations produce confident-sounding nonsense. Knowledge gaps generate plausible-but-wrong information. Edge cases trigger bizarre behaviour.
The emerging skill is identifying where AI will fail and designing systems that handle failures gracefully.
This means building human-in-the-loop checkpoints at critical decision points. It means implementing automated fact-checking layers for generated content. It means creating feedback loops that catch errors before they reach end users.
A prompt engineer who crafts beautiful prompts but ignores failure modes builds fragile systems. An AI engineer who understands failure patterns builds systems that degrade gracefully.
Skill Set 3: Knowing When NOT to Use AI
This may be the most valuable emerging skill: recognising when AI is the wrong tool.
AI is expensive, inconsistent, and opaque. For many tasks, deterministic code provides better results at lower cost.
Date parsing with AI? Expensive and unreliable. Regex or a date-parsing library? Cheap and perfect.
Extracting structured data from templated documents? AI works but is overkill. Pattern matching works faster and more reliably.
Generating random IDs? AI can do it. But why would you?
The skill is understanding AI's cost-benefit profile well enough to choose appropriately. Sometimes the answer is "don't use AI for this."
Skill Set 4: Prompt-to-Code Migration
When a prompt has been "solved"—it works reliably, it's been validated thoroughly, the use case is stable—the next step is often migrating from prompt to code.
Customer support email categorisation might start as a prompted GPT call. Once the categories are stable and the approach is validated, it becomes a fine-tuned model with lower latency and cost. Or it becomes a traditional classifier trained on the examples the LLM helped generate.
The skill is recognising when a prompt-based solution has matured to the point where it should become something more permanent.
Skill Set 5: AI-Human Interface Design
The way AI presents its output affects whether humans use it appropriately.
When should AI show confidence scores? When should it present alternatives rather than single answers? How do you communicate uncertainty without undermining trust?
These are design questions, not prompting questions. The skill is creating interactions that help humans understand AI's limitations whilst still capturing its value.
Chaos's suggestion interface, for instance, doesn't present recommendations as certainties. It shows options, explains reasoning, and makes it easy to override. This design acknowledges that AI judgment is imperfect and creates space for human correction.
Will Prompt Engineering Be Automated?
Partially, yes. And sooner than most prompt engineers expect.
Anthropic's prompt improver tool already suggests revisions to prompts that underperform. OpenAI's playground includes prompt optimisation suggestions. Third-party tools like PromptPerfect and PROMPTIST automatically refine prompts for specific objectives.
These tools won't replace all human judgment—context and nuance still matter—but they'll automate the mechanical aspects of prompt refinement. The difference between a good prompt and a great prompt, for most tasks, will be closed by automated tooling.
My estimate: 60% of current prompt engineering work will be automatable by end of 2025. The remaining 40% requires judgment about goals, context, and constraints that tools can't yet capture.
Is Prompt Engineering a Real Long-Term Career?
LinkedIn job postings for "Prompt Engineer" peaked in May 2023 at approximately 3,200 open positions globally. By January 2025, that number had fallen to approximately 1,700—a 47% decline.
The job title is fading because the skills are being absorbed into adjacent roles. "AI Engineer" captures the broader technical work. "AI Product Manager" captures the strategic direction. "Data Scientist" and "ML Engineer" absorb the implementation details.
"Prompt Engineer" as a standalone role is following the same trajectory as "Webmaster" in the early 2000s. When the web was new and mysterious, organisations hired webmasters to handle "the website stuff." As web literacy spread and web development professionalised, the generalist role dissolved into specialised functions.
Prompt engineering is experiencing the same dissolution. The skills remain valuable, but they're becoming table stakes for AI-adjacent roles rather than a standalone discipline.
If your career plan is "become a prompt engineer," reconsider. If your plan is "become someone who builds with AI and prompting is one of many skills," you're on more solid ground.
What Should I Learn Instead of Traditional Prompt Engineering?
The skills that will matter in 2026 and beyond:
Priority 1: Python fundamentals. AI systems need to be integrated, orchestrated, and customised. This happens in code. You don't need to be a senior software engineer, but you need to be comfortable with APIs, data processing, and basic scripting.
Priority 2: LLM API landscape. Understand the major providers (OpenAI, Anthropic, open-source models via Ollama/Hugging Face), their strengths and weaknesses, their pricing models, their rate limits. Know when to use which model for which task.
Priority 3: AI workflow tools. LangChain, LlamaIndex, semantic routers, vector databases. The ecosystem is evolving rapidly, but understanding the categories of tools matters even as specific tools change.
Priority 4: Evaluation and testing. How do you measure whether an AI system is working? How do you catch regressions? How do you A/B test prompt changes? This unglamorous skill separates production-ready systems from demos.
Priority 5: Domain expertise. AI plus your field is more valuable than generic AI skills. A lawyer who understands AI builds better legal AI than an AI expert who doesn't understand law. Deep domain knowledge becomes a moat as generic AI skills commoditise.
Resources worth your time:
- DeepLearning.AI's short courses on LangChain and AI agents
- Anthropic's prompt engineering documentation (free, excellent)
- Building LLM Apps with Python (various O'Reilly resources)
- The AI Engineer newsletter by Swyx and community
The Prompt Engineering Timeline: What Dies When
Q1 2025 (Now):
- Explicit output formatting via prompts → Structured output APIs
- Elaborate persona descriptions → Minimal or no personas
Q2 2025:
- Few-shot examples → Zero-shot with better base models
- Lengthy system prompts → Shorter, higher-level instructions
Q3 2025:
- Chain-of-thought scaffolding → Baked into model behaviour
- Manual prompt iteration → Automated prompt optimisation
Q4 2025:
- Negative instructions → Guardrails and filters
- Prompt refinement as manual process → AI-assisted prompt development
2026 and Beyond:
- "Prompt Engineer" as job title → Absorbed into "AI Engineer"
- Text prompts as primary interface → Multi-modal and agentic interactions
Why This Is Actually Good News
The obsolescence of prompt engineering skills isn't a crisis—it's progress.
When individual techniques become unnecessary because models handle them automatically, the floor rises. More people can use AI effectively. The barrier to entry drops.
The skills being automated were always workarounds for model limitations. As limitations disappear, so do the workarounds. This is how technology should work.
The analogy: we don't write assembly code anymore. We use higher-level languages that compile to assembly. This abstraction enabled vastly more software to be built by vastly more people. The assembly experts weren't destroyed; they moved up the stack.
Prompt engineering experts should do the same. Move from technique-level work to system-level work. Move from getting output from models to building reliable applications. Move from crafting prompts to architecting AI systems.
The opportunities in the new layer—AI engineering, AI product design, AI-augmented domain expertise—are larger than the opportunities in the old layer. The skill shift is real, but it's upward, not away.
How Should I Future-Proof My AI Skills?
Learn fundamentals, not techniques. Techniques expire. Understanding how LLMs work—attention, context windows, token probability—transfers across model generations.
Build systems, not prompts. A portfolio of working applications matters more than a library of clever prompts. Systems demonstrate the judgment that automated tools can't replicate.
Specialise with depth. "AI generalist" becomes less valuable as AI literacy spreads. "AI applied to X domain" becomes more valuable as applications mature.
Stay adjacent to automation. The skills at risk of automation tomorrow are the ones that are most routine today. Continuously move toward work that requires judgment, context, and creativity.
Contribute to tools and frameworks. Open-source contributions to AI tooling create reputation and keep you current. The people building LangChain and LlamaIndex understand the cutting edge better than anyone using those tools.
For Course Creators and Educators
If you're teaching prompt engineering, your curriculum needs updating:
Shift from techniques to principles. Instead of "here's how to write chain-of-thought prompts," teach "here's how to think about structuring reasoning tasks." Principles transfer; techniques expire.
Include obsolescence awareness. Tell students which skills have short half-lives. Prepare them for continuous learning rather than one-time mastery.
Focus on judgment over execution. When to use AI, which model for which task, how to evaluate quality—these decisions require judgment that automation can't easily replace.
Build in system-level thinking. Move from "how to write a prompt" to "how to build an application that uses prompts as one component."
The prompt engineering course I taught in March 2023 is substantially different from what I'd teach today. And what I teach today will be different again in 2026. If your curriculum hasn't changed in six months, it's probably falling behind.
The Shift Continues
Prompt engineering had a remarkable two-year window as a distinct skill set. That window is closing—not because the skills were wrong, but because the technology they supported is maturing.
The knowledge workers who adapt will move from prompt engineers to AI engineers. The educators who adapt will shift curricula toward principles and systems. The organisations that adapt will stop hiring for "prompting skills" and start hiring for "AI judgment."
The transition isn't scary. It's the normal evolution of any technical field. Assembly programmers became C programmers became Python programmers. Web designers became UX designers became product designers. Prompt engineers will become... something else. Something more valuable.
The future isn't less opportunity in AI. It's different opportunity. The people who recognise that shift earliest will position themselves best for what's coming next.
Chaos is built with AI workflow architecture, not prompt tricks. Multi-step AI orchestration that actually works reliably—no manual prompting required. See how modern AI engineering looks in practice. Start your free 14-day trial.