Originally published on bizzit.pl by CM Sławomir Chojnacki, President of Bizzit S.A. Republished with permission.
FM Michał Fudalej, founder of ChessboArt, serves as a Member of the Supervisory Board at Bizzit S.A.
Strategic Players and the Future Job Market
For years, chess clubs inside companies were treated as a pleasant curiosity — somewhere between team integration and a hobby. That is beginning to change, and for a very concrete reason. Companies deploying systems based on large language models are discovering that technical knowledge of AI is only half the story. The other half is the ability to formulate precise, multi-layered instructions that actually work — and here it turns out that an experienced chess player holds an advantage that cannot easily be acquired on a weekend course.
This thesis may sound provocative. But if you look closely at what effective work with language models actually requires, and compare it with what years of chess practice develop — the list of overlaps becomes long and anything but coincidental.
What Prompt Engineering Really Is
Prompt engineering is not typing questions into a chatbot. It is designing the structure of communication with a probabilistic system that responds to context, tone, order of information, and dozens of other factors simultaneously. A good prompt engineer knows that the same goal can be reached in a hundred different ways — and that most of them deliver worse results than a handful of well-considered ones.
This work requires, among other things:
- understanding the structure of a problem before formulating it,
- anticipating how the model will interpret a given instruction,
- breaking complex tasks into sequences of smaller steps,
- testing hypotheses and drawing conclusions from errors,
- linguistic precision — every word matters,
- keeping the final goal in mind while constructing each individual stage.
Viewed through the lens of chess — this is exactly what every player above amateur level does at every game.
Variant Thinking — The Foundation of AI Work
One of the first things an ambitious chess player learns is calculating variations: before making a move, the player mentally processes a tree of possibilities — what happens if I play this, what if I play that, how will my opponent respond, and then what? A club-level player sees three or four moves ahead. A strong player sees ten or more.
In working with language models, this mechanism has a direct equivalent. Rather than sending a single prompt and hoping for the best, an experienced specialist plans the entire sequence of interactions in advance. They know that in step one the model must be embedded in context, in step two the components of the problem must be separated out, in step three each must be analysed individually, and only in step four should a synthesis be requested.
This technique — known as chain-of-thought prompting or prompt chaining — is in practice the same cognitive process as sequential planning in chess. A chess player learns it through thousands of games. Transferring that mental habit to work with AI is not a metaphor — it is a transfer of a real competency.
Pattern Recognition and What Lies Beneath the Surface
Research by Herbert Simon and William Chase in the 1970s showed something that revolutionised the understanding of expertise: chess grandmasters are not better because they calculate faster. They are better because they see a position as a collection of meaningful patterns, not as a set of 32 pieces on 64 squares. Chunking — grouping information into meaningful units — allows them to process complex positions many times faster than amateurs.
In prompt engineering, patterns carry similar weight. An effective specialist recognises:
- which prompt structures yield predictable results for specific types of tasks,
- when the model is likely to veer in the wrong direction and why,
- which output formats are more reliable in given contexts,
- which words and constructions activate different registers of the model’s knowledge.
That knowledge does not come from documentation — it comes from dozens of hours of experimentation and analysis of results. A chess player acquires knowledge in exactly the same way: not from a textbook, but from positions, games, and mistakes. That same approach transfers to working with AI almost without modification.
Post-Game Analysis as a Model for Iterative Improvement
Chess culture is a culture of ruthless analysis of one’s own mistakes. After every game — won or lost — players return to the position, look for the moment where it went wrong, and test alternatives. Post-mortem is not optional; it is the obligation of anyone who wants to improve.
In working with language models, exactly the same mode of working produces exactly the same results. Recording prompt histories, categorising incorrect model responses, testing corrections and measuring their impact — this is prompt engineering at a professional level. And it is precisely what a chess player does after every game, simply in a different domain.
Crucially, a chess player is not frustrated by a mistake. They are interested in it. That disposition — in which failure is a source of data rather than a reason to give up — is one of the hardest attitudes to cultivate in AI work.
How a Chess Player Solves a Complex AI Task — Step by Step
To move beyond theory, it is worth seeing what these competencies look like in practice. Take the following task: “Prepare a risk analysis for a digital transformation project at a financial sector company.”
Step 1: Opening — Embedding the Model in a Role
Rather than throwing the task in directly, the chess player begins with a system prompt: assigning the model the role of an experienced consultant, setting the level of detail, defining the intended audience of the document. This is the equivalent of choosing a chess opening — a decision that determines the character of all the work that follows.
Step 2: Development — Building the Foundation Before Conclusions
Before assessing risks, the chess player asks the model to identify categories — they do not jump to conclusions without first decomposing the problem. Just as in chess: you develop your pieces before you attack.
Step 3: Tactics — Separate Analysis of Each Element
Each risk category receives a separate, dedicated prompt. Dispersing the model’s attention across many threads at once lowers the quality of each — exactly like scattering an attack across the entire board instead of concentrating on one sector.
Step 4: Coordination — Integrating the Results
Only once the partial analyses have been gathered does the chess player instruct the model to synthesise — with an explicit instruction regarding format and prioritisation. The equivalent of coordinating pieces toward a shared goal in the final phase of a plan.
Step 5: Verification — Checking the Combination for Weaknesses
Finally, a prompt from the reversed perspective: “Critically evaluate this analysis from the viewpoint of someone who would want to challenge it.” A chess player always checks whether their combination has a tactical weakness. Here they do exactly the same.
The result of this approach is incomparable with a single one-shot query — the analysis is deeper, more coherent, and more robust against objection.
Decomposing Complexity — From Whole to Parts and Back
In chess there is a fundamental distinction between strategy and tactics. Strategy is the plan across many moves: weaken the pawn structure, seize the open file, build a position for the endgame. Tactics is the specific sequence that executes that plan. A strong player moves between these two levels fluidly — knowing what they want and knowing how to achieve it step by step.
In prompt engineering this ability is equally essential. Strategy is understanding the ultimate goal — what should happen with the model’s output, who will read it, what decision it is meant to support. Tactics is the concrete construction of the prompt: what role to assign, how to divide the problem, what to constrain, what to emphasise. Most AI users operate exclusively at the tactical level — they type an instruction without broader context. Chess players instinctively think on both levels simultaneously.
Precision and Economy — No Move May Be Wasted
In chess there is a principle of economy: a good move should accomplish several goals at once — attack, defend, activate a piece, prepare a plan. A move that does only one thing is generally weaker than a move that does three things simultaneously.
In prompts the same logic produces measurable results. Every sentence should add value: defining a role, specifying a format, including an example, imposing a constraint, or formulating a goal. A prompt that does this concisely and multi-functionally yields better results than an elaborate instruction full of repetition and generalities.
Chess players have a deep-seated aversion to wasted moves. They transfer it naturally into an aversion to wasted words — and that is an advantage that is difficult to acquire without thousands of hours of practice.
Perspective Thinking — Understanding How the Model “Sees” a Prompt
One of the more important techniques in chess development is asking: “What does my opponent want?” When a rival makes a move, a strong player does not only ask what was played, but why — what plans it opens, what response it anticipates, what it is trying to achieve.
In working with language models, exactly the same approach leads to what researchers call model perspective — the ability to think about how the system will interpret a given prompt, what associations it will activate, what default assumptions it will make. The best specialists ask themselves: “If I were a model trained on billions of internet documents, how would I understand this instruction?” — and that cognitive empathy allows them to pre-emptively avoid misinterpretations.
A chess player trains this ability at every game throughout their entire chess life.
Bridge and Go — Related Competencies, a Different Dimension
Chess is a game of perfect information. Bridge and go add further layers that have their own equivalents in AI work.
A bridge player operates under conditions of incomplete information — they do not know their opponents’ cards. They must infer hands from the bidding and play, while simultaneously communicating their own intentions through the strictly limited language of conventions. This is a direct analogy to working with a model as a system whose internal states are inaccessible — one must infer behaviours from observing outputs and communicate intentions through carefully chosen prompt language.
A go player learns something less present in chess: thinking about the whole system above local battles. A local defeat can be a deliberate choice in favour of a global win. In complex AI systems — pipelines, agents, automated workflows — this systemic perspective is enormously valuable: optimising one prompt can degrade the results of another, and decisions about context have non-linear effects on the entire system.
Why Programmers Are Not Enough
Intuition suggests that the best prompt engineers should be programmers. Practice regularly overturns this intuition. Programmers tend to treat language models like deterministic systems — they expect that a precise instruction will yield a precise result. Language models do not work that way. They are probabilistic, contextual, and sensitive to nuances that no compiler would ever notice.
Moreover, programming does not train imagination or conceptual flexibility — and those are precisely the qualities that distinguish the best prompt engineers. Chess players have spent years working with a system that has its own logic, its own tendencies, and unpredictable reactions — and they learn to work with it productively rather than trying to control it. That is a fundamental difference in approach.
Chess Skills vs AI Market Requirements
Mapping chess competencies directly onto what the AI job market is looking for:
- Calculating variations → designing multi-step prompt chains
- Pattern recognition → identifying optimal structures, predicting model behaviour
- Post-mortem analysis → iterative debugging and refinement of prompts
- Two-level thinking (strategy / tactics) → designing prompt architecture for complex systems
- Economy of moves → precision and conciseness of instructions
- Perspective thinking → anticipating model interpretation, avoiding misreadings
- Combinational imagination → visualising output before it is generated
- Metacognition → detecting hallucinations, calibrating model confidence
- Tolerance for iteration and error → agile workflows, rapid prototyping
None of these competencies is isolated — together they form a cognitive profile that is precisely what companies deploying AI are searching for and cannot find often enough.
A New Kind of Value in the Job Market
The AI job market is looking for something it has not yet learned to describe well in job postings. Questions about specific tools and frameworks are slowly giving way to questions about how a candidate thinks — whether they can decompose a complex problem, whether they work methodically under pressure, whether they are capable of critically evaluating results they themselves generated.
In this paradigm, an ELO rating is beginning to be a more reliable signal than many a technical certification. Not because chess resembles programming. But because a high ELO is documented evidence of dozens of cognitive competencies that cannot be faked or acquired in a few weeks.
Companies that recognise this first will gain access to a group of employees with an exceptionally rare profile. Chess players, bridge players, and go players who realise the value of their own competencies in this context will be several steps ahead of the market — before the market even understands who it is looking for.
Originally published on bizzit.pl · Author: CM Sławomir Chojnacki, President of Bizzit S.A. · Republished with permission by ChessboArt.
FAQ
Will every chess player be a good prompt engineer?
Not every player — but the competencies developed through conscious, regular play create a very solid foundation. A chess player with a rating of 1600+ ELO and the motivation to learn the specifics of language models has real, measurable advantages over most candidates without that background.
Is prompt engineering a career with a future, or a passing trend?
The specific name of the profession will evolve as models develop. But the fundamental need for effective, thoughtful communication with AI systems — designing, testing, and optimising instructions — will not disappear. The tools will change; the cognitive competencies will remain.
Which prompt engineering techniques are closest to chess thinking?
Above all: chain-of-thought prompting, tree-of-thought, prompt chaining and few-shot learning. All require sequential planning and structural thinking — competencies that chess trains directly.
Is it worth listing chess skills on a CV when applying for AI roles?
Yes — but with an explanation. Rather than writing “hobby: chess”, it is worth writing: “Active chess player with ELO rating [X]; developed capacity for sequential planning, pattern recognition, and iterative problem-solving in complex systems.” This translates a hobby into the language of competencies.
Chess That Belongs on Your Wall
If chess strategy interests you as much as it does us — explore our wall-mounted chess sets, designed for players who take the game seriously in every sense.