Career-Ready ChatGPT Course with Real Projects & AI Skills
Tools & Skills
- ChatGPT basics - Playground, prompts, LLM fundamentals
- Advanced prompting - API usage, tool integrations
- Intelligent apps - Embeddings, RAG, custom GPTs
- Multimodal & deployment - Image AI, Docker/cloud, ethics
Ready to take your first step towards a rewarding career?
Contact WingSlide Technologies today to learn more about our programs and discuss how we can help you achieve your goals!
Get In Touch
ChatGPT + AI Tools Course Curriculum
1.1 ChatGPT & Generative AI Fundamentals
• Course Concepts: Evolution of ChatGPT, LLM architecture (transformers, tokens, embeddings), AI/ML/NLP basics, business use cases, limitations
• Hands-on: Sign up for OpenAI, explore Playground, write basic prompts (Q&A, summarization, translation).
1.2 Prompt Engineering Basics (Month 1)
• Topics Covered: Prompt structure, few-shot prompting, instructions, chain-of-thought techniques → improves quality in content creation, summarization, code generation
• Practice: Compare outputs with different prompt styles; iterate to enhance responses.
2.1 Master Prompting (Month 2)
• Advanced Techniques: Context management, nuanced prompting, scenario-based strategies, expert-level tips
• Exercises: Design prompts for multi-turn conversations, troubleshooting, knowledge extraction.
2.2 ChatGPT & AI Tools Integration
• API Module: OpenAI API basics, Python integration, building chatbots using Flask or FastAPI
• Toolchain Workflows: Automate content creation in spreadsheets, presentations, email (e.g., Excel + ChatGPT Plugin, Power Automate).
3.1 Retrieval-Augmented Generation (RAG) with Embeddings
• Core Concepts: Embeddings, vector databases (ChromaDB, Pinecone), similarity search → part of robust AI pipelines
• Build: Fast prototype—a chatbot retrieving answers from domain documents using LangChain.
3.2 Custom GPTs & Fine-tuning
• Custom GPTs: When to fine-tune vs prompt-tune; use cases, dataset prep
• Hands-on: Fine-tune a GPT model for domain-specific tasks (e.g., technical support, content generation).
4.1 Image & Multimodal AI Tools
• Image Generation: DALL·E / Stable Diffusion fundamentals, prompt strategies, style consistency
• Multimodal Apps: Combine text + image outputs (e.g., blog drafts with generated visuals).
4.2 Deployment, Scaling & Responsible AI
• Deployment: Containerization basics (Docker), serverless APIs, cloud deployment (Heroku, Azure, GCP)
• Ethics & Security: Bias detection, fairness, RLHF fundamentals, data privacy best practices
- Custom Chatbot for a specific domain (e.g., support assistant).
- RAG-based Q&A System with document ingestion and semantic search.
- Text + Image Generator App — a mini-blog creator with visuals.
- Full-stack Deployment — host an AI service via API/UI with versioning and scaling.
