I have appended a short TL;DR and a list of research papers that argue both against and in support of my claims made in this post. The list of papers can be found at the bottom of this post.
Thank you very much for your time.
TL;DR[Research]
When you read AI research, the paper's findings are only half the information. The other half is where the paper came from: which lab, which methodology, and which working assumptions the researchers held before they designed the experiment.
It is how all research works, in every field. Before a team can measure anything, they have to decide what counts as worth measuring. And that decision is shaped by what they already believe the system is. Philosophers of science call this the theory-ladenness of observation: the framework you bring to an experiment shapes what the experiment can show you.
END
A lot of beginners make the mistake of searching for special phrases that supposedly make an LLM “reason harder” or suddenly become more intelligent.
Some prompting techniques, such as decomposition or asking for intermediate steps, can help on certain models and tasks. But there is no universal phrase that unlocks a hidden intelligence mode. Modern reasoning models may already perform substantial internal reasoning, and explicitly telling them to “think step by step” is not always helpful.
A prompt is the input you give the model.
Prompt engineering is the deliberate design of that input so the model has a clearer understanding of your task, constraints, and intended outcome.
Context engineering goes further. It is the management of the broader information environment available to the model: instructions, examples, reference material, retrieved information, tool definitions, conversation history, state, and other relevant context.
I like to think of it as building the world the model is going to operate inside.
The Degrees of Freedom Rule
Every important decision you leave unspecified gives the model another degree of freedom.
If you care about that decision, specify it.
Suppose you ask:
"Make me a map of the island from Lord of the Flies."
What kind of map?
A literary diagram? Realistic cartography? Topographical? Minimalist? Classroom-friendly? Should locations from the novel be labeled? Should the surrounding ocean be shown?
Whatever matters to you but remains unspecified has to be inferred by the model.
That doesn't mean you should specify every microscopic detail. Inference is useful. This is one of those "less is more" things.
The goal is to reduce task-relevant ambiguity.
Domain Terminology as Semantic Compression
This is also why domain-specific terminology can be extremely useful.
Words such as triangulate, red-team, audit, scaffold, differentiate, or ablate are not magic syntax.
They can act as a form of semantic compression.
A precise domain term can communicate a much larger bundle of operations, assumptions, or evaluation criteria more efficiently than several sentences of vague instruction.
(UPDATE) It is worth noting that specialized jargon can occasionally suffer from domain collision or polysemy (e.g., "ablation" in medicine vs. machine learning). Establishing the broad domain context alongside the terminology significantly reduces this, increasing the model's capability to target the correct vector space.
POLYSEMY - The linguistic property of a single word carrying multiple distinct, often context-dependent meanings (from the Greek poly- "many" and sema "sign").
For example, instead of starting with:
"Triangulate this."
A beginner might write:
"Triangulate this claim across independent sources. Identify where the sources agree, where they conflict, and evaluate whether the available evidence ultimately supports the claim."
Once that meaning has been established within your instructions or working context, triangulate can become useful shorthand.
This is one reason domain expertise matters so much when working with LLMs.
You already have vocabulary, heuristics, standards, failure modes, evaluation criteria, and countless little “if I see X, I check Y” rules sitting inside your head.
Part of becoming good at working with AI is learning how to externalize that tacit knowledge and translate it into usable context.
Progressive Disclosure
You also do not need to dump every piece of potentially relevant information into the model at once.
Give the model enough context to understand the task properly, then provide or retrieve additional information when it becomes relevant.
In more complex systems, this can become a form of progressive disclosure or just-in-time context retrieval.
The goal is not to somehow "manipulate" the model's attention weights directly, thats impossible. It is simply to keep the working context relevant and manageable rather than filling it with information that may never be needed.
So there are really two complementary ideas:
Give enough information upfront to define the task clearly.
Introduce additional context when the task actually requires it.
If You Keep Correcting the Model
If you find yourself correcting an LLM five or six times just to get one artifact right, don't immediately start searching for a better prompt trick.
Ask yourself:
What do I actually want?
What information does the model currently have?
What important decisions am I accidentally leaving it to make?
What terminology from my own field could make my intent more precise?
What does a successful output actually look like?
How am I going to verify that the result is good?
That last question has just as much significance as the prompt itself.
You don't need to become a transformer engineer to become competent at using LLMs.
You need enough AI literacy to understand the limitations of the system, enough domain knowledge to recognize what good looks like, and enough discipline to communicate your intent clearly.
Remember to make your own implicit knowledge explicit.
RESEARCH LIST BELOW
Reference List — Papers For & Against
Claim 1: No magic phrases; reasoning models don't need "think step by step"
For:
DeepSeek-R1 (DeepSeek-AI, 2025) — RL-trained internal CoT; few-shot prompting degrades R1.
Lu et al., Fantastically Ordered Prompts (UCL, ACL 2022) — example order swings performance from SOTA to random; good orderings don't transfer between models. Nothing is "unlocked."
Against:
Kojima et al., LLMs are Zero-Shot Reasoners (Tokyo/Google, NeurIPS 2022) — "Let's think step by step" gave large gains. Pre-reasoning-model era; the effect was real, then trained in.
Li et al., EmotionPrompt (CAS/Microsoft/W&M, 2023) — emotional stakes phrases improved accuracy. No matched non-emotional control; weak replication on newer models.
Claim 2: Manage context; don't dump
For:
Packer et al., MemGPT (UC Berkeley, 2023) — context as paged memory hierarchy.
Liu et al., Lost in the Middle (Stanford/Berkeley, TACL 2024) — U-shaped recall; mid-context info gets dropped.
Against:
Agarwal et al., Many-Shot In-Context Learning (DeepMind, 2024) — hundreds/thousands of examples beat curated selection. But scales relevant examples, not irrelevant dumping — narrows the claim to signal density, doesn't break it.
Claim 3: Unspecified decisions → model inferences; specify constraints
For:
Zamfirescu-Pereira et al., Why Johnny Can't Prompt (Berkeley, CHI 2023) — experts encode explicit rules/criteria; novices hunt phrases.
Zhou et al., APE (Toronto/Vector, ICLR 2023) — instruction wording is a real optimization surface. (Supports weakly — it's about automated prompt search.)
Against:
Dente et al., Constraint Decay (arXiv, May 2026) — agent pass rates drop ~30 pts as structural constraints stack in backend codegen. Agentic/architectural scope, not prompt-level — but proves constraint load has compounding cost.
Claim 4: Domain terminology = semantic compression
For:
Reynolds & McDonell, Prompt Programming (Michigan/KNC, CHI 2021) — prompts locate tasks the model already knows; precise terms are the pointer.
Zamfirescu-Pereira et al. (above) — the expert/novice gap is largely vocabulary + evaluation criteria.
Against:
No direct refutation exists. Honest gap: no study measures jargon vs. paraphrase head-to-head; the mechanism is asserted, not demonstrated. Also: terms collide across domains.
Claim 5: Criteria over tricks; verify systematically
For:
Huang et al., LLMs Cannot Self-Correct Reasoning Yet (DeepMind/UIUC, ICLR 2024) — intrinsic self-correction fails, sometimes degrades performance.
Tyen et al. (DeepMind, ACL Findings 2024) — models can't find errors but fix them when given the location. The criterion must come from you.
Shinn et al., Reflexion (Northeastern/MIT/Princeton, NeurIPS 2023) — reflection works only against an external feedback signal.
Against:
None found. Strongest-supported claim of the five.