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Prompt EngineeringFine-TuningProduction AI
LLM Prompt Engineering for Real Results
Master Advanced Prompting & Custom Model Tuning for Production-Ready Applications.
Stop "chatting" with AI and start engineering it. This course moves beyond basic instructions to help you build robust, predictable, and scalable LLM implementations. You will learn to bridge the gap between a prompt that "sometimes works" and a system that delivers consistent, high-quality output for real-world apps.
What You Will Master
Move beyond "Act as a..." to multi-variable framing.
Use few-shot & many-shot learning to force model alignment.
Ensure LLMs return valid JSON/Markdown every single time.
Manage prompts as code in a production environment.
Implement Zero-Shot CoT and Self-Consistency strategies.
Build reflection loops where the AI critiques its own output.
Optimize RAG (Retrieval-Augmented Generation) context windows.
Reduce costs and latency with Token Optimization.
Create high-quality JSONL training pairs for fine-tuning.
Validate tuned models using BLEU, ROUGE, and "LLM-as-a-judge".
Curriculum Overview
Real Results Guarantee
Every module concludes with a "Production Stress Test" to ensure your solutions don't break in the real world. You will build tangible assets.
Master Prompt Library
A plug-and-play collection of production-tested templates for 10+ industries.
Fine-Tuning Capstone
A documented project where you prepare, train, and deploy a specialized model.
LLM Performance Dashboard
A framework for tracking accuracy, latency, and cost per request.
Student Reviews
4.9(850 total)
"This is the first prompt engineering course that actually treats prompts like production code. The JSON enforcement, self-reflection loops, and stress tests were 🔥. I’ve already replaced half our brittle prompts at work with what I learned here."
R
Rohit Verma
ML Engineer
"Loved the real-world framing and the performance dashboard idea. Some sections (BLEU/ROUGE) went a bit deep for non-ML folks, but overall it helped me communicate better with engineers and design more reliable AI features."
A
Ananya Gupta
Product Manager
"This course saved us money and embarrassment. Token optimization and RAG context strategies alone paid for the course in one week. Not fluffy at all—very “ship it to production” mindset."
S
Sahil Khan
Startup Founder
"The “prompts as code” module was gold. I would’ve liked a few more concrete examples in Node/Python, but the concepts translated easily. The fine-tuning capstone was surprisingly practical."
N
Neha Sharma
Full-Stack Developer
"Solid content, but definitely not beginner-friendly. If you’ve never worked with APIs or JSON, you might struggle. Once I caught up, the reasoning and CoT strategies were very useful."
A
Arjun Mehta
Data Analyst
"Finally, a course that explains why prompts fail and how to fix them systematically. The self-consistency and reflection loops changed how I design LLM systems for clients. Highly recommended for professionals."
P
Priya Nair
AI Consultant
"The RAG optimization and token cost breakdowns were excellent. Some lectures felt dense, but that’s expected at this level. This is not “ChatGPT tips”—it’s real engineering."
K
Kunal Singh
Automation Engineer
"Very powerful techniques, but I felt a bit overwhelmed at times. I was hoping for more content-creator-friendly examples. Still, it helped me understand how serious AI systems are actually built."
S
Simran Kaur
Tech Content Creator
"The production stress tests are the secret sauce. Anyone can write a prompt that works once—this teaches you how to make it work every time. The prompt library alone is worth the price."
A
Aditya Rao
Backend Architect
"Great balance between theory and hands-on work. The fine-tuning section clarified a lot of confusion I had about JSONL datasets. Would love a follow-up course focused purely on evaluation and benchmarking."
M
Mohit Bansal
Freelance Developer
Frequently Asked Questions
Full Lifetime Access
Certificate of Mastery
Prompt Templates
Private Discord
Production Ready Strategies
Instructor
Celoris
Celoris Designs
Pioneering Agentic Workflows
Expert engineering team specializing in multi-agent orchestration and autonomous LLM workflows.
4.9(850+)
12 hours Content
Prerequisites
- Basic understanding of AI and LLMs
- Familiarity with JSON and data structures
- Basic coding knowledge (Python/JavaScript helper)
- Access to an LLM API (OpenAI, Anthropic, or similar)