KISSES in AI: Why Traditional KISS is Not Enough for AI Products (My Real Experience)
Let’s talk about something we all know in product development – KISS.
Keep It Simple, Stupid.
For years, this rule has saved me so much time and stress. When I was building regular apps and software, whenever we followed KISS, things just worked better. The product was easy to use, the team stayed happy, and we could launch faster. No unnecessary features, no over-complicated code, just focus on solving the real problem.
But when I started building AI products, I quickly realised – normal KISS is not enough 😀
AI is different. It doesn’t think like us. It only follows patterns and guesses the next word. One small mistake in your prompt and the whole output goes wrong. After working on several AI projects for Indian startups, I discovered that we need a stronger version of KISS specially for prompt engineering and AI systems.
That’s how KISSES was born.
KISSES = Keep It Simple, Short, Explicit & Structured
This small change has completely improved the way I build AI products. Today I’m sharing the full story and practical examples so you can start using it too.
Why Traditional KISS Works Great in Normal Product Development
- Focus on one main problem at a time
- Remove extra features that nobody needs
- Make things easy for the user
- Save time, money and team energy
I have seen this in my own projects , simple products always win in the market.
But AI Products Need More – That’s Why imagined KISSES
In AI, your prompt is the actual “code”. If the prompt is messy, the AI output becomes messy. After facing many failed experiments, wasted API money and weird hallucinations, I realised we need two extra things:
Explicit : tell the AI exactly what you want Structured : tell the AI exactly how to reply
So I added “ES” to KISS and made it KISSES. This is not theory. This is what I use every single day while building AI for real Indian companies.
Breaking Down KISSES : One by One
1. Keep It Simple
One prompt = One clear job.
Bad example (too many tasks): “Hey AI, analyse this customer feedback, find sentiment, suggest improvements, make a roadmap and draft an email to the boss.”
Good way (step by step):
- Step 1: Find top 5 repeating problems
- Step 2: Rate each as High, Medium or Low impact
- Step 3: Give one simple fix for each
- Step 4: Write a short email for the team
Simple steps = better control and easy debugging. Just like adding masalas one by one while cooking dal!
2. Keep It Short
Long prompts confuse AI and cost more money.
Bad example: “We are a fast-growing SaaS startup in India and Southeast Asia trying to reduce churn based on the data below…”
Good example: “Give 3 practical ways to reduce churn for a B2B SaaS product in India.”
Short = cheaper + clearer. In Indian startups where every rupee matters, this saving is huge.
3. Keep It Explicit
AI guesses if you don’t tell it clearly.
Vague prompt: “Summarise this report.”
Explicit prompt: “Summarise the report in 5 bullet points. Include key numbers. Professional tone. Maximum 120 words.”
For RAG systems (when AI reads your own documents): “Use ONLY the provided documents. If answer not found, say ‘Not available in provided documents.’ No assumptions. Limit to 150 words.”
Explicit prompts reduce hallucinations and help follow privacy rules (very important in India now).
4. Keep It Structured
Give AI a clear format so every answer looks the same.
Bad prompt: “Create a project summary.”
Structured prompt: “Create a project summary with these 5 points:
- Objective
- Scope
- Timeline
- Risks
- Next Steps Total under 200 words.”
For technical use (JSON format): “Return answer in this exact JSON format:
{ “risk_score”: “”, “approval”: “”, “reasons”: [], “confidence”: “” }”Structure makes AI output ready for your backend system.
My Real Production Insight
In all the AI projects I have done so far, 90% of problems came from:
- Unclear prompts
- Too much extra information
- No output rules
- No fallback plan
Very rarely from the AI model itself.
So before you pay for GPT-4o or any new model, first make your prompts follow KISSES. You will see magic.
Quick KISSES Checklist (Use this every time)
- Is there only ONE main objective?
- Can I remove 20% of extra words?
- Is the output format clearly mentioned?
- Did I set the length?
- Did I tell what to do if information is missing?
If yes to all – you are ready!
Final Thought
In normal software, too much complexity shows as visible bugs. In AI, unclear prompts create hidden problems that you discover only later.
That’s why I believe every AI builder in India should adopt KISSES. It is not about being clever. It is about being clear. And clarity always wins.