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The end of prompt engineering: why your teams must stop talking to AI and start commanding it

· 20 min read · Paul-Antoine Tual

context engineering prompt engineering AI MATIA Method SME AI agents

By Paul-Antoine TUAL (AI Transformation Leader, Croissance et Transitions). Updated 19 May 2026.

The founding misunderstanding

On 30 November 2022, OpenAI launched ChatGPT. The interface looked like a messaging app. You wrote in it as you would write to a colleague. The machine replied with surprising politeness, nuance, sometimes humour. And one word settled into the vocabulary of businesses: prompt. People learned to “talk to AI properly”. Teams were trained in “the perfect query”. A job title was coined: prompt engineer.

Three and a half years later, in May 2026, this word has had its day. Not because artificial intelligence has disappointed: it has never performed better. But because the analogy that carried it, talking to AI as if to a human, was an illusion of interface, not a technical truth. Andrej Karpathy, former head of AI at Tesla and a guiding figure of the field, wrote so publicly as early as June 2025 on X: he now prefers the term context engineering to prompt engineering, because “prompt suggests a short task description, whereas in every industrial LLM application what matters is the delicate art of filling the context window with exactly the right information for the next step” [1]. Tobi Lütke, head of Shopify, backed the same shift [2]. In the summer of 2025 Gartner published a note whose title sums up the pivot: Lead the Shift to Context Engineering as Prompt Engineering Fades [3].

This text rests on a simple thesis. A large language model is not a human. It has no intuition, no goodwill, no memory of the previous conversation unless it is re-injected. It does one thing, and one thing only: at each step, it computes the most probable next token given all the tokens that precede it. “Talking” to it as to a human is a useful cultural convention for the general public; in a business, it is a strategic mistake. The right way to instruct it, in 2026, is the way one adopts with any desktop computer: clear, explicit, structured instructions. And the form best suited to carry these instructions on the market’s dominant models, Claude (Anthropic), GPT-5 (OpenAI), Gemini (Google), is structured markup: XML, JSON or explicit delimiters depending on the model and the task.

This is not an opinion. It is what the vendors themselves recommend in their official documentation. It is what independent benchmarks measure. It is what every team that puts AI into production practises, without formalising it. And it is, for a French SME that wants to draw real value from its AI tools in 2026, the most poorly understood lever on the market.

What an LLM really is, for a leader who does not have time to read a research paper

To understand why the “talking to a human” analogy is misleading, you only need to look under the bonnet. No mathematics. Three mechanisms are enough.

The tokenizer. Before a model “reads” your sentence, a deterministic program cuts it into small pieces called tokens. A token is neither a word, nor a syllable, nor a character. It is a statistical unit learned from billions of pages of text. Each token is given a unique identifier number in a vocabulary of the order of 100,000 to 200,000 entries [4]. When you write “Draft the minutes of a meeting”, the machine does not see your sentence: it sees a sequence of numbers.

The transformer. This is the neural architecture that has prevailed since 2017. Its defining feature: it takes the sequence of tokens as input, and produces a single thing as output: a probability distribution over the next token. The model does not “understand” your question. It computes: given this sequence of numbers, what is the most probable next number? Then it chooses. Then it starts again. Word by word, token by token, it generates its answer [5].

Attention. At each step, each token “looks at” the other tokens in the sequence and computes how much it should rely on each one. When a model processes the word “lawyer” in “the client consulted their lawyer”, attention weights the tokens “client” and “consulted” more than “the”. This is also why the format of the input matters as much as its content [6, 7].

An important nuance deserves to be stated here, because it directly conditions practice: a model’s attention does not treat every position in your prompt as equally valuable. A now well-documented phenomenon, sometimes called context rot or lost in the middle, shows that transformers massively favour the beginning of the query (primacy effect) and the end of the submitted text (recency effect). The probability that a crucial piece of information buried in the middle of a long narrative query is correctly taken into account can drop markedly as the context lengthens: work on long contexts measures major losses of usable reliability [26]. Practical conclusion: the longer and less marked-up the prompt, the more attention goes astray. Explicit markup is not a typographic affectation: it is a map you give to the attention mechanism so that it does not get lost.

There, in three mechanisms, is what an LLM is: a statistical splitter, a probabilistic predictor, an attention orchestrator. None is anthropomorphic. None “understands” in the human sense of the word. The academic debate on this point remains open [8, 9]. But for an SME leader who must decide how to train their teams, the practical conclusion is clear: the machine responds better to what looks like program instructions than to what looks like a café conversation.

Why the chat interface has distorted the debate

The analogy error was born of the interface, not the technology. Before ChatGPT, language models were APIs consumed by developers, in scripts, with formatted inputs. In November 2022, OpenAI made two product choices that changed collective perception: the chat window, and the “helpful assistant” tone. The first creates the expectation of a conversation. The second creates the illusion of intent.

The cognitive-science literature describes this phenomenon under the term anthropomorphism. Conversational interfaces reinforce this bias through the simulation of turn-taking, artificial response delays, and first-person vocabulary (“I think that…”) [10, 11]. A recent review speaks of a “double-edged sword”: anthropomorphism eases adoption, but it masks the crucial differences between humans and LLMs, which leads to over-confidence in the answers and a poor calibration of uses in business [11].

For an SME, this bias has a concrete cost. When a leader believes they must “talk to ChatGPT properly”, they steer their training towards the rhetoric of the query: polite phrasing, an “act as if you were an expert” example, promises of reward. Some of these recipes circulated massively on LinkedIn between 2023 and 2025. Recent studies show that most bring no measurable gain in accuracy, and that some degrade performance [12].

The 2025-2026 turning point: from “prompt” to “instruction”

What changes in 2025-2026 is that the vendors themselves are formalising the other path. Three signals converge, and they must be set in a broader frame. The DORA 2025 report (Google Cloud), which draws on nearly 5,000 responses from tech professionals and more than 100 hours of qualitative interviews, highlights a significant dissonance: while ~90% of developers say they use some form of AI assistance and 80% consider that it increases their individual productivity, organisational delivery indicators often remain flat: individual gain without systematic collective gain [27]. The gap between adoption and real productivity at the organisation level is the clearest indicator of the method problem.

Anthropic. The official Claude documentation explicitly recommends the use of XML tags to structure prompts. The wording is unequivocal: “Claude has been specifically trained to pay close attention to your structure” [13]. The guide cites use cases: , , wrapping each indexed , , and to distinguish reasoning from the answer.

OpenAI. The GPT-5 guide is explicit: “GPT-5 interprets prompts literally and exhaustively”, and recommends “structured XML specifications such as <[instruction]_spec>” to improve instruction-following [14, 15]. The model is tuned for precision: it will do exactly what is written, without liberal interpretation. This makes ambiguous instructions more costly in hallucinations than before.

Google. Gemini’s official Prompting Guide makes the same recommendation: frame instructions, examples and reference content with explicit tags or separators, because the model uses these boundaries to focus its attention on the right portion of the context [16].

Beyond the vendors, academic research produced several works in 2024-2025 that consolidate this finding. StructEval (arXiv 2505.20139, 2025) proposes a comprehensive benchmark of LLMs’ ability to produce structured outputs: state-of-the-art models reach 75/100 on average, and quality varies strongly with the requested format, which implies that specifying the format in the prompt is a performance lever in its own right [17]. Meaning Typed Prompting (arXiv 2410.18146, 2024) shows that a typed, structured specification of outputs improves reliability and reduces inference cost [18]. XML Prompting as Grammar-Constrained Interaction (arXiv 2509.08182, 2025) proposes a theoretical framework: XML markup acts as a grammar constraint that reduces the space of possible outputs, and therefore variance: a formal demonstration that a structured prompt is not an aesthetic whim, it is a reduction of entropy [19].

On the figures side, several sources converge, and they must be qualified to avoid over-extrapolation. Practitioners’ analyses report that well-placed XML markup can reduce hallucinations by up to 40% on certain tasks [13]. A study published in 2024 in npj Digital Medicine (PMC11039454) on the interpretation of hepatology guidelines (hepatitis C) documents a jump in accuracy from 43.0% (GPT-4 Turbo alone) to 99.0% with a RAG framework combined with structured prompt engineering [20]: a strong result, but specific to this precise clinical use case; generalisation to any business task must be made with caution. On the security dimension, Anthropic published in late 2025 that Claude Opus 4.5 reduces the rate of successful prompt injections to ~1.4% in its browser-agent benchmark under new safeguards, versus ~10.8% for Claude Sonnet 4.5 under old safeguards [21]. Isolation through markup between instructions and data is one of the defences.

The honest counter-argument: when markup is not the answer

It must be said clearly: XML markup is not a magic formula applicable everywhere. Intellectual rigour requires presenting the counter-argument as it exists in the literature.

A benchmark published in May 2026 by Manish Ramavat compared, on Claude Sonnet 4.5, 150-token extraction prompts in two versions: flat prose and XML-marked-up prose. Result: the XML version costs 31% more input tokens for a negligible accuracy gap of −1.2 percentage points [22]. At 10,000 calls per day with this type of prompt, the XML overhead represents around $515/year wasted on Sonnet 4.5.

The same author’s conclusion deserves to be read in full: “If your prompts are long, complex, multi-section, or handle untrusted inputs, use XML. If they are short, clear and templated, do without.” Wrapping a 10,000-token document in XML tags costs 4 additional tokens, but lets attention cleanly isolate the document from the instructions. The benefit/cost ratio therefore tips in favour of markup as soon as the context grows more complex.

Simon Willison, one of the most closely followed practitioners on these subjects, makes the same observation at a broader level. His recent study (9,649 experiments across 11 models and 4 formats: YAML, Markdown, JSON, TOON) shows that no format dominates universally, and that the model’s familiarity with the format matters as much as the structure itself: the ultra-compact TOON format, little present in training corpora, paradoxically loses tokens because the model “hesitates” to follow it [23].

The operational rule that emerges from this, and which must be kept in mind, is therefore nuanced: structured markup becomes essential as soon as the task is complex, the context extensive, the sources multiple, or the inputs potentially untrusted. For simple uses (summarising an email, rephrasing a paragraph), clear prose is enough. But in business, few uses remain simple once the exploration phase is over.

What if the 2026 models had become good enough to understand natural language without XML?

This is the most legitimate objection a leader can raise, and it deserves a direct answer. Yes, Claude Opus 4.7, GPT-5.5 and Gemini 3.1 Pro understand natural language infinitely better than their 2023 predecessors. A free-prose prompt, on a simple case, will very often give a good answer on the first try.

But three forces mean that the discipline of structured instruction remains relevant, and becomes even more so with these models.

First, the more literal interpretation. OpenAI’s official GPT-5 and GPT-5.5 guides say so explicitly: these models interpret prompts “literally and exhaustively”. A vague instruction will no longer be “softened” by the model; it will be executed to the letter.

Second, the stake is no longer one successful query, it is 10,000 reproducible queries. In solo exploration, a free-prose prompt works nine times out of ten. In production, over 10,000 calls per day, the 10% gap represents 1,000 non-compliant outputs per day, unacceptable for a business process.

Third, markup also structures human thinking. A team that cannot formulate the four zones “role / context / instructions / output format” cannot clearly formulate its business request either. The rigour of the format reveals the rigour of the thinking.

That said, the verdict 24 to 36 months from now will shift. If the next generation of models internalises a native understanding of vague intentions, the boundary will move. The prudent rule: for the next 18-24 months, structured markup is the standard; beyond that, to be re-assessed.

Why this changes everything for an SME in 2026

If the good practice in 2026 is no longer to “talk to AI properly” but to command it with structured instructions, several leadership decisions follow.

First, on team training. According to a survey published by a French training organisation (2026), fewer than 12% of employees have received structured training in prompting, and those who benefit from it produce results that are around 40% more accurate [24]. But the quality of the training matters more than its existence. Training that teaches how to write long polite sentences, promise rewards, make the model “play a role”, will be obsolete in six months. Training that teaches how to specify a task, structure an instruction, mark up a context, define an output format is durable.

Second, on the prompt templates used in production. Professional practice in 2026 consists of building libraries of versioned XML templates, shared across teams, tested on evaluation sets, audited at each model update. This looks far more like source-code management than like writing.

Third, on the governance of autonomous agents. The stake rises a notch as soon as AI is no longer consulted but delegated to. An autonomous agent (one that calls tools, writes files, sends emails) executes a stream of instructions composed in a chain. If the instructions are conversational, the slightest vagueness opens the door to aberrant behaviour. If they are structured and marked up, the agent stays on its rails [25].

Fourth, on the relationship with the vendor. An SME that masters the structured specification of its tasks is less captive to the model. A correctly written XML template runs, with a few adjustments, on Claude, on GPT-5, on Gemini, even on local open-source models. Portability is an underestimated strategic advantage.

Three questions to ask this very week

For an SME leader reading this article, here are the three questions that turn the observation into immediate action, to put to your IT department, your AI champion or your provider:

  • Do you have an inventory of the critical prompts currently used in production, and who is named as responsible for them? If the answer is “no” or “everyone”, there is a prompt-governance debt to open.

  • When was the last non-regression test on your AI’s outputs, that is, the verification, on a standardised set of examples, that the answers remain compliant after a model or prompt update? If the answer is “never”, you are exposed to silent drift.

  • If you had to migrate tomorrow from Claude to GPT-5 or the other way round, how many prompts in your internal catalogue would you have to rewrite entirely? If the answer is “all of them” or “we do not know”, your portability is weak.

These three questions require neither tool nor budget. They reveal the organisation’s real level of AI maturity, independently of the number of tools deployed.

What the MATIA Method™ recommends

The MATIA Method™ (the proprietary methodology of Croissance et Transitions, built around five levels of AI maturity) addresses this question within its level 3 (Orchestre): the industrialisation and operational governance of use cases. Three practices are laid down as a foundation.

1. A standardised minimum template. For any AI use in production, the prompt is written by explicitly distinguishing four zones: the role and the task, the reference context, the method instructions, the expected output format:

You are a senior analyst tasked with… … …

  1. First check the consistency between the documents
  2. Identify the points of divergence
  3. Propose a synthesis in …

<output_format> Answer in English, structured in three sections, with [n] citations referenced to the documents. </output_format>

This structure fits in a few dozen additional tokens. On a long prompt, it is amply amortised. On a short prompt, it is trimmed down: this is the 80/20 rule of markup.

2. A library of versioned templates. Critical prompts are stored in a versioned repository, with their test sets, their change history, their named owner. This is the discipline of application code. This library is organised around a simple structure, sometimes codified under the acronym CARE: Context, Action expected, Result, End-goal. Four creation processes coexist in business: steering by subject-matter experts (SME-Driven) for legal and financial uses; open participation (Crowdsourcing) for creative cases; AI-assisted generation then human filtering (AI-Generated) for large-scale optimisation; structuring by role (Role-Based) to standardise an entire department [28].

3. A delegation framework for agents. Junyr Agents™, the flagship product of the Junyr suite operated on junyr.app, embodies this discipline: delegation of AI agents that can be operated, triggered and audited by email via the Email Routing layer of Junyr Mail™. Each agent is defined by an XML instruction template, its tools are limited to an explicit scope, its outputs are constrained to a schema.

Conclusion: command, do not converse

The industry is turning a page. The word prompt will survive a few more years in everyday vocabulary. But in business, in 2026, professional practice is aligning on a more rigorous discipline: you do not talk to AI, you command it. With explicit instructions, marked-up contexts, specified output formats, versioned templates, and documented governance. Structured markup (XML, JSON or explicit delimiters) is the de facto standard on which the three big vendors converge.

For a French SME that wants to draw real value from its AI tools this year, the most structuring lever is not a better model. It is a discipline of instruction. The 2026 window remains open: 18 to 24 months to switch from an AI that is used to an AI that is architected. What is at stake is neither fear nor urgency; it is mastery. And mastery, as always, begins by changing the right word: here, replacing talk with command.

Frequently asked questions

Is prompt engineering really dead? No. The word survives in everyday vocabulary and in some job descriptions. But professional practice has shifted: people now speak of “context engineering”, encompassing structured markup, context management, template design and prompt governance. “Prompt engineering” in the narrow sense gives way to a discipline of orchestration.

Do you really have to use XML everywhere? No. The nuanced rule is: use structured markup (XML, JSON or explicit delimiters) as soon as the task is complex, the context extensive, the sources multiple, or the inputs potentially untrusted. For simple uses, clear prose is enough. The Ramavat benchmark of May 2026 documents that on short prompts (≈150 tokens), XML markup can be a useless overhead.

What is the difference between prompt engineering and context engineering? Prompt engineering focuses on the formulation of a given query at a given moment. Context engineering encompasses the whole filling of the context window: task description, few-shot examples, retrieval results (RAG), multimodal data, available tools, state, history.

My team is non-technical. Do they need to be trained to write XML? Not directly. You train them instead to specify a task, structure an instruction, mark up a context, define an output format. The XML templates are then carried by an AI champion, a developer or a consultant, and the teams fill them in, they do not write them each time.

Will XML markup age with the 2027 models? Probably, in part. If models internalise a greater understanding of vague intentions, the boundary between clear prose and strict markup will move. For the next 18-24 months, structured markup is the standard; beyond that, to be re-assessed. The lasting benefit is not XML in itself, it is the discipline of specification.

Does XML markup replace RAG, fine-tuning or system prompts? No. They are complementary. RAG injects the company’s private data into the context; XML markup cleanly separates them from the instructions. Fine-tuning adjusts the model; markup structures the instruction. Modern system prompts are themselves structured markup in disguise.

Going further

  • MATIA Method™ Audit (AI Express Audit & Roadmap): a 60-minute video call to assess your current maturity level. croissance-transitions.fr

  • Junyr Agents™: delegation of AI agents for SMEs, operable and auditable by email. junyr.app

  • Junyr Mail™: eIDAS professional email. junyr-mail.com

Sources

Paul-Antoine TUAL is an AI Transformation Leader. He heads Croissance et Transitions (SAS) and operates the Junyr™ suite, MATIA Method™ (methodology), Junyr Agents™ (AI agents for SMEs, junyr.app), Junyr Mail™ (eIDAS email). He supports the leaders of French mid-caps and SMEs in their AI transformation, 60-minute diagnostic: croissance-transitions.fr.

Paul-Antoine Tual

Paul-Antoine Tual

AI Transformation Leader · MATIA Method™ · Transition manager specialising in AI for French SMEs and mid-caps. Engineer from the École des Mines de Nantes, lawyer, developer since 1993.

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