After using AI extensively for the last 1-2 years, I've noticed an interesting limitation.

When I ask AI to generate 10 test cases for a scenario, usually only 3-4 are highly relevant. The remaining test cases are often generic, duplicated, or based on assumptions that don't match the actual business scenario.

The problem isn't just accuracy—it's also cost(Extra Money, Water, Electricity etc).

Even the irrelevant output consumes tokens, which means: • More tokens used • Higher API costs • More time spent reviewing and filtering responses

I don't think the solution is to stop using AI. Instead, we need to use it more efficiently.

A few things that have helped me: ✅ Provide complete business context and acceptance criteria. ✅ Mention constraints and assumptions explicitly. ✅ Ask for only "Top 5 high-priority test cases" instead of asking for everything. ✅ Generate test cases iteratively rather than in one big prompt. ✅ Ask AI to first identify missing information before generating the answer. ✅ Use AI as an assistant, not as a replacement for domain knowledge.

AI is incredibly powerful, but better prompting and better context are still the keys to saving both tokens and money.

Has anyone else experienced the same thing while using AI for testing or development?

Have you tried testing a feedback loop with specific domain tweaks? Curious if it improved relevance!

[Score: 97/100 | 100% Attendance] Ready to Deploy Next-Gen AI & Automation Globally 🚀

Building powerful AI models is one thing. Integrating them into seamless, autonomous enterprise workflows is where the real value lies.

I am thrilled to share that I have officially graduated from the Ruangguru Engineering Academy+ AI Engineering Bootcamp (Batch 11) with a Certificate of Excellence (Grade A, Score: 97) and a 100% attendance record! 🏆

Throughout this rigorous program, I specialized in bridging the gap between core AI development and enterprise automation. My technical toolkit now includes: ⚙️ AI Automation & Agents: Engineering autonomous Agentic AI systems and orchestrating complex production workflows using n8n. 🧠 Generative AI: Designing robust RAG (Retrieval-Augmented Generation) architectures, fine-tuning LLMs, and implementing advanced NLP solutions. 👁️ Core Deep Learning: Training and deploying Computer Vision and Deep Learning models powered by PyTorch and Python.

From building foundational ML algorithms to creating production-ready, AI-driven automated workflows, I am fully equipped to deliver high-impact AI engineering to global teams.

🌍 To International Tech Leaders & Recruiters: I am actively looking for global/remote roles as an AI Engineer / Automation Engineer. If you are looking for an engineer who brings top-tier execution and reliability, let’s connect or reach out via DM!

A huge thank you to my mentors and peers at Ruangguru and Skill Academy Pro for this incredible journey.

#AIEngineering #AgenticAI #n8n #AutomationWorkflow #LLM #RAG #PyTorch #DeepLearning #ComputerVision #Python #GlobalTalent #TechHiring