Artificial Intelligence with Prompt Engineering
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Follow on Instagram for UpdatesArtificial Intelligence with Prompt Engineering Syllabus — Unit-wise Content
The official GTU curriculum for Artificial Intelligence with Prompt Engineering (subject code DI05016011) as prescribed for Biomed · Semester 5 divides the course into 5 units, covering 9 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (DI05016011)Unit 1 Foundations of Artificial Intelligence and Generative AI
- 1.1 Introduction to Artificial Intelligence 1.1.1 Definition and history of AI 1.1.2 AI vs Machine Learning vs Deep Learning 1.2 Types of AI 1.2.1 Narrow AI vs General AI 1.3 Applications of AI 1.3.1 AI in daily life 1.3.2 AI in education 1.3.3 AI in healthcare 1.3.4 AI in cybersecurity 1.4 Introduction to Generative AI 1.4.1 Concept of Generative AI 1.4.2 Types of Generative AI systems 1.4.3 Text generation
- Image generation
- Code generation 1.5 Generative AI tools 1.5.1 ChatGPT 1.5.2 Google Gemini 1.5.3 DALL·E 1.6 Basics of Natural Language Processing 1.6.1 Concept of NLP 1.6.2 Role of NLP in chatbots and LLMs Program Name: Diploma Engineering Level: Diploma Branch: Information Technology Subject Code: DI05016011 Subject Name: Artificial Intelligence with Prompt Engineering
Unit 2 Basics of Large Language Models (LLMs)
- 2.1 Introduction to Large Language Models 2.1.1 Concept of LLMs 2.1.2 Training data and parameters 2.2 Working of LLMs 2.2.1 Tokens 2.2.2 Embeddings (conceptual) 2.2.3 How LLM predicts the next word 2.3 Popular LLM Models 2.3.1 GPT models 2.3.2 Claude 2.3.3 LlaMA 2.3.4 Gemini 2.4 Capabilities and Limitations 2.4.1 Hallucination problem 2.4.2 Context window limitations 2.4.3 Bias in AI systems 2.4.4 Cost of AI models
Unit 3 Prompt Engineering Fundamentals
- 3.1 Introduction to Prompt Engineering 3.1.1 Definition and importance 3.1.2 Prompt lifecycle 3.2 Prompt Structure 3.2.1 Instruction 3.2.2 Context 3.2.3 Input data 3.2.4 Output format 3.3 Prompting Methods 3.3.1 Zero-shot prompting 3.3.2 Few-shot prompting 3.3.3 Role-based prompting 3.3.4 Instruction prompting 3.4 Prompt Design Best Practices 3.4.1 Writing effective prompts 3.4.2 Prompt testing and evaluation 3.4.3 Iterative prompt improvement
Unit 4 Prompt Engineering Techniques
- 4.1 Prompting Techniques 4.1.1 Chain-of-Thought prompting 4.1.2 Prompt chaining 4.1.3 Self-consistency prompting 4.1.4 ReAct prompting Program Name: Diploma Engineering Level: Diploma Branch: Information Technology Subject Code: DI05016011 Subject Name: Artificial Intelligence with Prompt Engineering 4.2 Step-by-step reasoning prompts 4.3 Prompt Engineering for Tasks 4.3.1 Text summarization 4.3.2 Content generation 4.3.3 Code generation 4.3.4 Question answering 4.3.5 Language translation 4.4 Retrieval Augmented Generation (RAG) 4.4.1 Concept of RAG 4.4.2 Using external knowledge sources
Unit 5 AI application development
- Generative AI, Agentic AI 5.1 Using AI Tools for Productivity 5.1.1 Writing emails 5.1.2 Generating reports 5.1.3 Creating presentations 5.2 AI in Software Development 5.2.1 Code generation 5.2.2 Code explanation 5.2.3 Code documentation 5.3 AI for Debugging Code 5.3.1 Finding errors in code 5.3.2 Fixing bugs using AI 5.4 Introduction to AI APIs 5.4.1 Concept of APIs 5.4.2 Examples: OpenAI API, Gemini API 5.5 Developing AI Applications 5.5.1 AI chatbot 5.5.2 AI blog writer 5.5.3 AI document summarizer 5.5.4 AI question generator 5.6 Introduction to Agentic AI 5.6.1 Concept of AI agents 5.6.2 Autonomous task execution 5.6.3 Examples: AutoGPT
- CrewAI 5.7 Responsible AI 5.7.1 Ethical AI usage 5.7.2 Bias and fairness
- Data privacy 5.7.3 Risks and limitations of AI systems
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