Over the last year, I've spent a significant amount of time building with large language models—integrating them into internal tools, experimenting with local GPU hosting, and using them to accelerate engineering workflows.

Like many engineers, I initially thought prompting was about "asking clearly." It's not. Prompting is about conditioning probability.

Once I understood how modern LLMs actually work internally, my output quality improved dramatically. In this article, I'll explain what makes a good prompt—not from a surface-level "tips and tricks" angle—but from the perspective of how transformer models function under the hood.

A Quick Refresher: How Modern LLMs Actually Work

At runtime, an LLM like ChatGPT does something deceptively simple. It breaks your input into tokens, converts tokens into embeddings which are vectors, runs them through transformer layers using self-attention, predicts the next token, and repeats until completion. That's it.

There is no symbolic reasoning engine. No stored "facts database." No internal goals. It is a massive next-token probability machine trained on internet-scale text.

So when we talk about "good prompts," what we really mean is a prompt that shapes the probability distribution in your favor.

Quality #1: Low Entropy Instructions

If you write "Explain AI," the model faces enormous uncertainty. What level? What audience? Business or technical? Historical or modern? The internal probability distribution is wide and unstable.

Now compare that to "Explain transformer architecture internals to a backend engineer familiar with distributed systems, using concrete examples." This narrows the distribution significantly. Why this works: it reduces entropy, reduces branching token paths, and activates a narrower semantic cluster. A good prompt reduces ambiguity before generation even begins.

Quality #2: Clear Role Framing Activates Latent Clusters

LLMs are trained on blog posts, StackOverflow, research papers, technical documentation, and conversational Q&A. These patterns form clusters in latent space. When you say "You are a senior distributed systems architect," you are activating formal technical tone, tradeoff discussions, structured explanations, and system-level thinking. The model doesn't "become" that person. It shifts toward that statistical region of training data. Role framing is latent space steering.

Quality #3: Structured Output Reduces Generation Drift

Transformers generate text sequentially. If you specify "Give 5 bullet points with a short explanation for each," you create a predictable structural pattern: dash, line, explanation, repeat. This reduces randomness in formatting and keeps the answer tight. Without structure, the model may over-elaborate, wander into tangents, or change tone mid-way. Structure acts as a generation constraint.

Quality #4: Positive Constraints Work Better Than Negative Ones

Consider "Don't be vague." The model must represent "vague," negate it, and produce something else. This introduces internal uncertainty. Now compare that to "Provide 3 concrete examples with metrics." This directly biases toward numbers, specific entities, and measurable outcomes. LLMs respond more reliably to positive instructions than abstract negations.

Quality #5: Explicit Audience Improves Precision

Internally, LLMs weigh vocabulary and abstraction level based on context. If you do not specify audience, it defaults to mid-level general internet tone. If you specify "For CTOs," "For junior developers," "For product managers," or "For ML researchers," you influence terminology depth, assumed prior knowledge, and example complexity. Audience is one of the most powerful prompt levers.

Quality #6: Decompose Tasks for Better Reasoning

LLMs simulate reasoning by expanding intermediate tokens. When you say "Think step by step," you encourage the model to generate intermediate reasoning tokens and build context internally before final answer. This reduces shallow answers and improves logical consistency. Internally, it increases attention depth before output convergence.

Quality #7: Keep Critical Instructions Near the End

Transformers use self-attention across all tokens, but recency bias exists. If your key constraint is buried in a long prompt, it may get diluted. A bad pattern would be 1,000 tokens of context with the final instruction at the top. Better is context, then final explicit task at bottom. The most recent tokens strongly influence next-token prediction.

Quality #8: Avoid Competing Objectives

A bad prompt example would be "Be concise but also very detailed." This creates conflicting generation pressures. Internally, the model oscillates between short completion cluster and deep elaboration cluster. Better would be "Provide a concise summary in 120 words, followed by a detailed breakdown in bullet points." Now both objectives have structure and separation.

Quality #9: Few-Shot Examples Anchor Output

Transformers are elite pattern imitators. If you provide an example with input and output, the model continues the pattern. This works because the original training objective was next-token continuation. Few-shot prompting is powerful because it reshapes local probability mass, anchors structure, and reduces stylistic variance.

Quality #10: Control Length to Control Variance

If you don't specify length, the model fills uncertainty with verbosity. Specify word limits, bullet counts, or section count, and length constraints reduce output variance significantly. Think of it like setting bounds on a function.

What Makes a Bad Prompt?

From an internal transformer perspective, bad prompts have vague objectives, hidden assumptions, multiple conflicting instructions, no audience specification, no structural constraints, and overuse of abstract modifiers like "better," "smart," or "advanced." All of these increase entropy and destabilize token prediction.

Prompting Is Probability Engineering

As engineers, we should think of prompting like system design. You are not "asking clearly." You are conditioning P(next_token | context). A good prompt narrows entropy, activates the correct latent cluster, constrains structure, reduces ambiguity, and aligns with known training distributions. Once you see it this way, prompting becomes predictable instead of mystical.

A Practical Template I Use

For technical work, I use this structure: 

Role: You are a senior backend engineer.

Audience: Mid-level developers.

Context: System uses Node.js, PostgreSQL, and Kubernetes.

Task: Explain how to design a scalable job queue.

Constraints:
  - 5 sections
  - Include real-world tradeoffs
  - Under 800 words

Output: Structured blog-style explanation.

This works because it segments semantic instructions, reduces ambiguity, activates known blog-style patterns, and constrains generation paths.

Final Thought

LLMs are compressed representations of internet-scale probability distributions. They do not reason like humans. They simulate reasoning through structured token generation. A great prompt doesn't "ask better." It shapes probability better. And once you understand that, you stop guessing—and start engineering your prompts deliberately.