Prompt Engineering & AI Agent Training Course
Learn modern AI prompt engineering with Educad Academy. This course teaches you the core skills needed to design, refine, and deploy high-quality prompts for advanced AI models including GPT-4, Claude 3.7, Gemini 2, Grok 4, DeepSeek, and other frontier systems. You’ll build practical expertise in prompt design, reasoning techniques, multimodal prompting, and real-world AI workflows used in top industries worldwide.
Course Objectives:
- Give learners a solid understanding of modern prompt engineering concepts and how today’s leading AI models process instructions.
- Help you design clear, precise, and structured prompts that deliver reliable results across different AI systems.
- Build your ability to create advanced reasoning prompts for complex tasks, problem solving, and multi-step workflows.
- Teach you how to work with multimodal inputs, including images, video, documents, and visual reasoning.
- Train you to use tools, functions, memory, and agent workflows to build practical AI systems for real use cases.
- Show you how to generate high-quality synthetic data for training, fine-tuning, and evaluation.
- Improve your skills in debugging, benchmarking, and optimizing prompts for accuracy, speed, and cost.
- Prepare you to apply prompt engineering across industries like customer service, software development, finance, healthcare, and research.
- Guide you through building a fully functional AI agent as a capstone project, from design to deployment.
Course Content:
Module 1: Foundations of Modern Prompt Engineering
- Evolution of prompting: from GPT-3 to Grok 4, Claude 3.7, Gemini 2, DeepSeek R1, o3, etc.
- How frontier models (reasoning, tool-use, long context) changed prompting paradigms
- Zero-shot, few-shot, chain-of-thought, and the rise of automatic reasoning prompts
- Role-playing vs. system prompts vs. implicit reasoning
Module 2: Core Prompt Design Principles
- Clarity, specificity, and context window management (128k–1M+ tokens)
- Task decomposition and scaffolding
- Output formatting: JSON mode, structured outputs, delimiters, XML/YAML
- Tone, persona, and style control across models
- Temperature, top-p, presence/frequency penalties in practice
Module 3: Advanced Reasoning & Chain-of-Thought Techniques
- Chain-of-Thought (CoT), Tree-of-Thought (ToT), Graph-of-Thought (GoT)
- Self-Consistency, Majority Voting, and Monte-Carlo sampling
- Automatic CoT generation (Auto-CoT, Let’s think step by step → obsolete?)
- Plan-and-Solve, ReAct, Reflexion, and self-critique loops
- Long-context reasoning with needle-in-haystack and retrieval prompting
Module 4: Multimodal & Vision-Language Prompting
- Prompting vision models (GPT-4o, Claude 3.5/3.7 Sonnet, Gemini 1.5/2, LLaVA, Qwen-VL)
- Image + text reasoning, diagram interpretation, OCR-free document understanding
- Video prompting and frame sampling strategies
- Prompting for image generation (Flux, Imagen 3, Midjourney v7, Ideogram)
Module 5: Tool Use, Function Calling & Agentic Workflows
- Structured tool calling (OpenAI, Anthropic, Grok, Gemini)
- Building reliable agents with ReAct, OpenAI Swarm, LangGraph, CrewAI
- Parallel tool execution and result aggregation
- Memory systems: short-term, long-term, vector stores in prompts
- Multi-agent collaboration and debate protocols
Module 6: Prompting for Synthetic Data & Model Training
- Generating high-quality instruction datasets (Alpaca, Evol-Instruct, Orca-style)
- Self-Instruct, Constitutional AI, and preference data creation
- Synthetic data for RAG, fine-tuning, and distillation
- Evaluator/reward model prompting (MT-Bench, Arena-Hard, Promptriever)
- Distilling reasoning traces from o3/Claude 3.7 into smaller models
Module 7: Evaluation, Debugging & Optimization
- Automated evaluation: LLM-as-a-judge (Pairwise, Glicko-2, Prometheus)
- Benchmarking prompts (IFEval, GPQA, MMMU, MathVista, LiveBench)
- Prompt optimization techniques: O1-style reasoning search, DSPy, TextGrad, Evo- Prompt
- Hallucination detection and fact-checking prompts
- Safety, jailbreak resistance, and red-teaming prompts
Module 8: Production Prompt Engineering
- Prompt versioning, A/B testing, and observability (LangSmith, Helicone, PromptLayer)
- Caching, routing, and fallback strategies
- Cost optimization across model tiers
- Building prompt libraries and internal tools
- Security, PII redaction, and compliance prompting
Module 9: Real World Applications
- Customer support (multi-turn agents + knowledge base)
- Software development (cursor-style agents, code generation, review, testing)
- Finance & legal (contract analysis, risk assessment, compliance)
- Healthcare (clinical note summarization, differential diagnosis prompting)
- Research automation (paper writing, experiment design, literature review agents)
Module 10: Capstone Project
- Build a complete AI agent system (e.g., autonomous research assistant, legal contract reviewer, or coding co-pilot)
- Tasks: design prompt architecture → implement tool calling → add memory & RAG → evaluate → deploy with UI
- Present metrics, cost analysis, and failure cases
Learning Outcomes:
By the end of this course, you will be able to:
- Create clear, effective prompts for advanced AI models and real-world tasks.
- Apply structured prompting techniques for consistent and accurate outputs.
- Use advanced reasoning strategies to solve complex problems with AI.
- Work confidently with multimodal prompts for images, video, and documents.
- Build agent-style workflows with tools, memory, and function calling.
- Generate and refine synthetic datasets for training and evaluation.
- Evaluate prompt quality using benchmarks, automated judges, and debugging methods.
- Deploy optimized prompts and workflows in production environments.
- Build AI systems that support customer service, coding, finance, healthcare, and research.
Career Path Opportunities:
- Prompt Engineer
- AI Agent Developer
- AI Product Specialist
- AI Content Developer
- AI Automation Expert
- Generative AI Consultant
- Business Analyst (AI Enhanced)
- Freelance AI Solutions Specialist
- Software Developer (AI Integrated)
- Data & Research Assistant (AI-Driven)
International Student Fee: 350 USD
Flexible Class Options
- Corporate Group Training | Fast-Track
- Weekend Classes For Professionals SAT | SUN
- Online Classes-Live Virtual Class( L.V.C) Online Training
