This edition covers why current systems aren't scalable and the strategies you can apply to build scalable solutions.
👋 Welcome to all the new subscribers.
Over the past year, I’ve had the opportunity to work on a variety of online and offline production-grade AI systems—many running at scale, powering real-world SAAS platforms.
If you scroll through LinkedIn or Twitter, most “prompting” posts focus on:
- Getting better blog ideas
- Generating SEO-friendly content
- Or just playing around with ChatGPT
👉 That’s just scratching the surface.
Prompting in Production ≠ Prompting for Content
The right prompting system can:
✅ Slash linearly scaling inference costs
✅ Prevent failures on self-hosted AI setups
✅ Deliver far more accurate, reliable responses for real-time SAAS use cases
✅ Handle complex, multi-step workflows via chained or structured prompts
✅ Bridge the gap between LLMs and traditional software logic
This week, I posted about something I’ve been experimenting with
— Model Context Protocol (MCP) - a framework designed for scalable prompting systems in production environments.
— How are startups losing money to AI while scaling
Let’s Talk Real Scaling: What Does It Actually Take?
Most founders I talk to hit the same wall - They integrate an LLM, wrap a prompt, maybe even fine-tune something — and it works… kind of.
→ Then usage grows.
→ Requests jump.
→ Latency spikes.
→ Quality drops.
→ Costs balloon.
That’s when you realize: your “prompt” is now infrastructure.
Scaling an AI system isn't about getting better completions. It’s about designing systems where prompts evolve into predictable, testable, and extensible components — like APIs, not playground experiments.
Strategy 1: Nail Your LLM Output Configuration
Once you've picked your AI model (ChatGPT, Gemini, Claude, etc.), the next step isn’t more prompts — it’s getting your model settings right.
These small knobs control how your AI behaves, how fast it responds, and how much it costs.
Here’s what to know:
- Output length → More words = more cost + slower responses. Want short answers? Don’t just set a limit — design your prompt to guide it.
- Temperature → Controls how creative or predictable the AI is.
- Top-K / Top-P → These decide which words the AI is allowed to choose from. Think of them as filters that shape how smart or silly the response can get.
⚠️ Get these wrong, and you risk rambling answers, strange loops, or worse — high bills.
Start simple: temperature: 0.2, top-p: 0.9, top-k: 40 — works well for most structured outputs.
Strategy 2: Use Examples — One-Shot or Few-Shot Prompting
If you want better, more consistent AI responses — show it how by giving examples. This is one of the most powerful (and underused) prompting strategies in production AI.
→ One-Shot Prompting
You give just one example, and ask the model to do the same thing for new input.
It’s simple, and works best for clear, repeatable tasks.
Example: 🗣 “Classify this movie review as POSITIVE, NEUTRAL, or NEGATIVE.” Then show 1 labeled example before the real input.
→ Few-Shot Prompting
You give multiple examples — usually 3 to 5 — to show the model a pattern.
The model doesn’t just copy the format. It learns the pattern and applies it to the next case. Few-shot is great for:
- Classifications
- Text-to-structure tasks (like converting a pizza order into JSON)
- Edge-case handling
🧩 Real Tip: Quality > Quantity
Your examples should be: Clear , Well-formatted , Cover normal + edge cases
Even one sloppy example can throw the model off.
📌 Think of few-shot like giving the AI a mini onboarding doc: "Here’s how we do things. Now do it again — same format."
Strategy 3: Guide the AI with System, Role & Context Prompts
If you want better control over how your AI responds — tell it who it is, what it's doing, and why.
That’s the idea behind system, role, and contextual prompting. They sound fancy, but they’re super practical.
→ System Prompting – Big-picture instruction
This is like setting the mission. 🗣 “Classify reviews as POSITIVE, NEUTRAL, or NEGATIVE. Return the result in JSON.”
Useful for:
- Telling the model what format to respond in (e.g. JSON, bullet points)
- Controlling tone, safety, or structure
→ Role Prompting – Who the AI is
You assign a role: teacher, travel guide, code reviewer, etc. The model shapes its tone, knowledge, and response accordingly.
🗣 “You’re a travel guide. Suggest 3 museum visits in Amsterdam.” Change the role to “comedian,” and the answers get funnier.
→ Contextual Prompting – What the AI should know right now
You give specific background info to help it generate better responses.
🗣 “You’re writing for a retro gaming blog. Suggest 3 article ideas.”
Without that context, it might suggest something irrelevant like modern game reviews.
🧩 Together, these three prompting styles let you build AI outputs that are clear, on-brand, and task-specific — especially powerful when used in combination.
Strategy 4: Use Step-Back Prompting to Boost Clarity
Sometimes when you ask AI a direct question, you get something… generic. That’s where step-back prompting helps.
Instead of jumping straight to the final task, you first ask a general question to help the AI think more broadly — then use that answer to guide your main prompt.
It’s like warming up the model’s brain.
Why It Works
Large language models aren’t just pulling answers out of thin air — they’re pattern machines. If you give them a chance to reflect on general ideas first, they often come up with smarter, deeper answers when it’s time to get specific.
→ Simple Example
Direct Prompt: “Write a new level for a first-person shooter game.” → Result: Generic, kind-of-cool-but-not-that-unique.
Step-Back Prompt: First ask: “What are 5 great level themes in shooter games?” Then: “Now write a level based on one of those.”
✅ The second version gets more creative, detailed, and on-theme — because the model had something to work with.
🧩 Step-back prompting is especially useful when:
- You want richer ideas
- You’re designing anything multi-step (games, workflows, decision trees)
- You want to reduce hallucinations or vague outputs
It helps the model "zoom out," think more clearly, and then "zoom in" with purpose.
Strategy 5: Use “Chain of Thought” to Make the AI Think Step-by-Step
Sometimes when you ask AI a question, especially one that involves math or logic, it jumps to an answer — and gets it totally wrong.
That’s where Chain of Thought prompting helps. Instead of asking for just an answer, you ask the AI to “think step by step.”
→ Why it works
When you guide the model to show its reasoning, a few things happen:
- ✅ Answers get more accurate
- ✅ You can spot where it went wrong (better debugging)
- ✅ It works more consistently across different models
- ✅ You get cleaner, more robust results
→ Real Example
🗣 “When I was 3, my partner was 3x my age. Now I’m 20. How old is my partner?”
❌ Without step-by-step thinking, the model might say: “63” (just guessing).
✅ With Chain of Thought:
- When I was 3, partner was 3×3 = 9
- I’m now 20 → I aged 17 years
- So partner is 9 + 17 = 26
Boom. Correct.
🧩 Bonus Tip: Use Few-Shot + Chain of Thought
Want even better results? Give the AI one or two examples of how to reason before your actual question.
This helps it learn the pattern and apply it to your prompt more reliably — perfect for:
- Math and logic tasks
- Code generation
- Generating structured text from a seed idea
💡 If the task can be solved by “talking through” the steps in your head — prompt the AI to do the same. It works.
Strategy 6: ReAct – Make the AI Think and Do
Most prompts just ask AI to think. But what if it could also act?
That’s the idea behind ReAct prompting — short for Reason and Act. It lets the AI not only plan and reflect, but also take actions like:
- Searching the web
- Calling APIs
- Running code
- Updating its own plan based on new info
It’s like giving your AI a brain and a keyboard.
→ How It Works
ReAct creates a loop:
- Think → What do I need to do?
- Act → Perform a search or call a tool
- Observe → Read the result
- Repeat → Adjust the plan
This continues until the task is complete.
→ Real Example
Prompt: “How many kids do the members of Metallica have?”
With ReAct, the AI doesn’t just guess. It actually:
- Searches each band member’s name
- Reads the answers (like “James has 3 kids”)
- Adds them up
- Returns: “10 children total”
That’s multi-step reasoning + real-world action.
🧩 ReAct is perfect for:
- AI agents that combine tools and memory
- Complex tasks that need live info or iteration
- Search → reason → respond loops
You’ll often use it with frameworks like LangChain, Vertex AI, or OpenAI tools.
Heads up: ReAct does require more orchestration — you need to handle tool setup, manage input/output flows, and sometimes clean up long responses. But it’s the first real step toward building AI agents that work like humans do.
Some more strategies in my recent articles to develop scalable AI systems -
- MCP - Model context protocol
How to turn your agents for example voice, analysis into small soldiers that complete their work with scale and report back to the general. Here is my last week article -
https://www.linkedin.com/pulse/why-every-ai-startup-needs-mcp-rohan-girdhani-the-techdoc--vpkqc
2. How to correctly build a SAAS using AI -
https://www.linkedin.com/pulse/ai-built-my-saas-week-i-spent-3-months-rebuilding-rohan-9vnrc
3. Self hosted AI for cost optimization
Conclusion
Scaling AI is about more than just making things work as you grow. It’s about building smart, reliable systems from the start. By fine-tuning model settings, using efficient prompting techniques, and leveraging frameworks like MCP, you can optimize costs, improve speed, and ensure consistent performance.
AI in production requires constant refinement. Use these strategies to create systems that not only scale but thrive. Keep experimenting, stay agile, and ensure your AI delivers real value, no matter how big you get.
See you next Saturday.
Rohan.
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