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Generative AI (Prompt Engineering) – Basic Course

This 2-month program has been designed to help the learners build foundational expertise in Generative AI and Prompt Engineering. The learners learn to design effective prompts and automate content and data workflows. They are trained to apply AI models to improve marketing, analytics, and business productivity.

Why choose Cronus Consultants to learn Generative AI/Prompt Engineering?

At Cronus Consultants, mentors guide candidates in developing strong expertise in Generative AI and Prompt Engineering through structured learning and practical use cases. With expert guidance, learners learn to apply AI models effectively to advance their careers in emerging AI-driven roles.

Curriculum Overview

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Core Concepts: Transitioning from traditional Machine Learning to Deep Learning and Generative AI.
Technical Setup: Basic Python workflows, including NumPy and Pandas for data handling.
Neural Networks: Brief overview of the transformer architecture and how Large Language Models (LLMs) differ from standard predictive models.

LLM Mechanisms: Exploring models like GPT-4, Llama 3 (2026 versions), and Google Gemini.
Prompt Engineering: Advanced techniques including few-shot prompting, Chain of Thought (CoT), and ethical prompt design.
Fine-Tuning: Understanding when and how to fine-tune a model versus using it "out of the box".

RAG Architecture: Building systems that connect LLMs to private data sources to reduce hallucinations.
Vector Storage: Hands-on labs with tools like Pinecone or Weaviate for storing and retrieving high-dimensional data embeddings.

Image & Video Gen: Working with DALL-E, Midjourney, and Stable Diffusion for text-to-image/video workflows.
Code Generation: Using AI agents and tools like GitHub Copilot for code translation, review, and documentation.
Creative Assets: Practical applications in marketing, design, and audio generation.

Application Frameworks: Utilizing Lang Chain or Hugging Face ecosystems to build end-to-end AI applications.
Deployment: Deploying models as microservices using platforms like NVIDIA NIM or Google Vertex AI.

Responsible AI: Frameworks for mitigating bias, ensuring data privacy, and managing AI hallucinations.
Agentic AI: Exploring autonomous AI agents that can perform multi-step tasks without constant human input.