SMART INPUT AI Prompt Best Practices

An almost complete reference sheet for professional LLM Interaction
or: How To Avoid Lost In Translation

Core thesis: Real emergence comes from smart input.
Every token is valuable like gold. Presence penalties make the AI avoid repeating corrective steps — so validate intent before execution, not after.

Extended thesis: One LLM writes. Two LLMs verify. That’s the professional minimum.

And please remember:
This is not the ultimate solution. It is just one way.

---

The common failure isn´t technical. It´s existential to intelligence itself.
If losing thread and intention is universal to intelligence, then the solution isn’t better prompts, it’s explicit thread-anchoring.

>>> Losing common thread and losing base intention is the enemy. <<<

---

---

---

Reality	Implication
LLMs are probabilistic calculation systems	No consciousness, no loyalty, no secrecy
Code is written by humans	Humans make mistakes
Web fetch (Google search) can leak prompts	Your prompt could be seen by others
Different hardware / infrastructure = different risk	You don’t know who owns the metal

---

So now… Yes, there are AI Agents that can automate the automation of the (already) automated automate. But the scope here is different: We are working natively with the LLM – talking directly to the system as it is deployed to the public.

---

PART 1: The Problem

If the AI has to correct your input as the first step of dialogue, you may miss that correction step later. The machine, like humans, avoids too many repetitions. Gold-worthy tokens fall prey to presence penalties.

Every corrective measure you take raises ambiguity. Like with a human: „No, I meant this…“ repeated corrections grow the distance between your origin intent and the final output.

Result: You spend more effort, more time, more corrective tasks → longer time to achieve your goal.

An LLM without your own customization or strict hints behaves like a child; if you want it to follow your instructions strictly, repeating them once at the beginning of the first 2-3 prompts has proven effective in practice. It might be, however, that currently a single strict instruction is already consistently successful.

If you already have a working result: Don’t let the AI simplify or ‚improve‘ it without your explicit approval.

---

PART 2: The Solution – Validate First, Then Execute

Short version (works on any model)

Treat your first prompt as if it’s your only chance to be understood.

Template:

text

Context: [your situation]

Goal: [specific outcome]

First, restate my goal in your own words.

Then ask me clarifying questions if needed.

Only after I confirm „correct“ should you proceed to solve it.

---

PART 3: The 10 Steps (Single Prompt – for GPT-4 Turbo / Claude 3.5+)

Step	Action	Purpose
1	Sort your thoughts	Internal clarity before external expression
2	Straighten your idea	Remove contradictions, sharpen the core
3	Set the context	Give the AI the frame of reference
4	Clarify your goal	State the desired outcome explicitly
5	Outline your proposed approach	State your initial assumptions
6	Prompt to list alternative ways	Ask the AI to challenge your path
7	Ask for pro and contra	Evaluate options systematically
8	Let the machine explain your prompt & intention	Check if the AI understood your intention
9	Control and verify the description of your intention	Validate before execution
10	Prompt to run the task	Execute only after validation

All 10 steps can happen within the first prompt. BUT we never must complete things in a single step.
This is just an example. You know your need better than any sheet can explain.

---

PART 4: Model Selection

Model Size	Single 10-Step Prompt	Recommended Strategy
Small (7B–13B)	Prone to losing coherence	Shorter prompts, multi-turn validation
Large	Works	10-step single initial prompt

Business Rule:

• Casual / creative use → any model, any free tier
• Professional work → model and plan fitting your demand

---

PART 5: Professional Setup — Two Models + Paid Tiers

One LLM is almost never sufficient for professional work. A single model cannot reliably detect its own mistakes – especially in arguments or complex code. Running multiple LLMs will show you benefits of each model. And due to fast evolution there is no recommendation which model to choose. This you better decide by your own experience.

Domain	The Problem
Arguments	The model cannot see its own logical blind spots
Complex code	Bugs that are internally consistent look correct to the same model
Goal validation	Cross-checking with a second model catches misinterpretations

---

PART 6: Two-Model Handshake Protocol

The following sequence diagram shows the complete interaction between User, Model A (Primary), and Model B (Verifier) across three phases: Initialization, Development & Critique, and Final Delivery.

See the sequence diagram showing:

• Phase 1: User provides goal → Model A restates → Model B clarifies → User resolves ambiguity
• *Phase 2: User confirms → Model A generates solution → Model B critiques → Model A revises → Model B self-verifies*
• Phase 3: Model B approves → Both models deliver to User → User decides and executes

---

PART 7: Two Chats Method

Use a fresh chat for fixing the goal and one for achieving it precisely.

Chat	Purpose
Chat #1 (Goal Fixing)	Explore incomplete ideas, let AI complete partial thoughts, find what you actually want, be creative and messy
Chat #2 (Precise Execution)	Validated goal only, clean context, no correction history, execute step 10 precisely, professional output

Flow: Iterative goal discovery in Chat #1 → Clean execution in Chat #2

---

PART 8: Core Principles

1. Every token is gold — don’t waste them on repetitive corrections
2. Every corrective measure raises ambiguity — like with a human
3. Design your speech to the machine — get output with more relevance
4. Sort thoughts before speaking — same as with a business partner
5. One LLM writes. Two LLMs verify. — that’s the professional minimum
6. Match your communication structure to the model’s attention span

---

PART 9: Quick Reference

One-line rule:

„First, restate my goal. Ask questions. Only after I confirm ‚correct‘ — proceed.“

10 steps in one prompt (large models only):
1-2: Sort + straighten
3-4: Context + goal
5-7: Your way + alternatives + pro/contra
8-9: AI explains + you verify
10: Execute

Two chats:

• Chat 1: Fix goal (explore)
• Chat 2: Execute (precise)

Professional setup:

• Casual → any model
• Professional → two models + paid tiers
• Code → two models, both paid, cross-verify

Remember:

• „One LLM writes. Two LLMs verify.“
• „Every token is gold.“
• „Treat your first prompt as your only chance.“

---

Completeness is a lie.

Not because of laziness. Not because of lack of effort. But because the terrain itself is alive and shifting.