TL;DR: Most people treat Prompt Engineering like creative writing. This is why their AI implementations fail. True Prompt Engineering is not about "asking nicely"—it is about Constraints, Architecture, and Logic. Inside the labs of Anthropic and OpenAI, they don't "chat" with the model; they force it into submission using Negative Rules, XML structural tagging, and Chain-of-Thought forcing. Here is the playbook on how to stop guessing and start engineering.
James here, CEO of Mercury Technology Solutions.
There is a misconception I see in almost every company I met.
Executives think "Prompt Engineering" is about finding the right magical words—like casting a spell in Harry Potter.
They think if they say "Please be professional" or "Act like a world-class CEO," the AI will solve their problems.
This is wrong.
The best engineers at Anthropic and OpenAI don't "craft" prompts. They Reverse-Engineer them.
They treat the LLM not as a person, but as a stochastic probability engine that needs to be fenced in.
Here are the 6 internal techniques that separate the toys from the enterprise-grade tools.
1. Constitutional Prompting (The Power of "No")
Amateurs give Positive Instructions:
- "Write professionally."
Pros give Negative Constraints:
- "No jargon."
- "No sentences over 20 words."
- "No assumptions about domain knowledge."
The Logic: An LLM has infinite ways to "be professional" (many of them wrong). It has very few ways to "not use jargon."
Anthropic's internal research shows that Negative Constraints reduce hallucinations by ~60%.
You don't get performance by asking nicely; you get it by removing the paths to failure.
2. Reliability Before Magic (The Boring Truth)
This is the secret that 99% of companies learn only after burning $1,000,000.
Everyone wants an AI that can "Code the entire app" or "Analyze this 50-page legal contract."
They fail because they start with the hardest use case.
An AI that works 80% of the time sounds impressive in a demo.
In production, an AI that fails 20% of the time is Liability.
- The Mercury Approach: Pick a boring, repetitive task. Define the rules. Demand 99% accuracy.
- Only when you have reliability do you scale to complexity.
3. Chain-of-Thought Forcing
Never ask: "Explain your reasoning."
Instead, Force it via XML:
"Before answering, show your step-by-step thinking inside <thinking> tags."
This is how OpenAI debugs internally.
By forcing the model to "show its work" before it generates the final answer, you catch logic errors early. The act of writing out the logic actually improves the quality of the final output.
4. XML Output Parsers
Amateurs say: "Return in bullet points" or "Give me JSON."
Models ignore this ~30% of the time.
Pros use XML Enclosure:
XML
<answer> <main_point>X</main_point> <evidence>Y</evidence> <conclusion>Z</conclusion> </answer>
The Logic: Structure is harder for the model to break than formatting. This boosts compliance to nearly 98%.
5. Few-Shot Examples WITH Reasoning
Most people provide examples like this:
- Input: A --> Output: B
This teaches the model what to say, but not how to think.
Pros use:
- Input: A --> Reasoning: (Why A leads to B) --> Output: B
This teaches the model the Algorithm of Thought. This single trick boosts accuracy more than any "mega-prompt" you can buy online.
6. System Prompt Separation (The Guardrail)
- System = The Constitution (Rules)
- User = The Request (Variable)
- LLM = The Executor
If you mix rules and requests in one block, the user can "Jailbreak" the model by saying "Ignore previous instructions."
The Fix:
SYSTEM: "You are an editor. Rules: No new claims. Sentences < 18 words."
USER: "Here is the text to sharpen."
By separating the "Constitution" from the "Citizen," you prevent injection attacks and keep behavior consistent.
Conclusion: The Reframe
AI doesn't fix chaos. AI amplifies chaos.
If your business process is undefined, adding an LLM will just create undefined output at the speed of light.
The companies winning in 2026 aren't the ones with the "coolest" prompts.
They are the ones building Boring Foundations.
Reliability first. Complexity second. Scale third.
That is the only way to play.
Mercury Technology Solutions: Accelerate Digitality.
