Introduction to Prompt Engineering

Session 1: Fundamentals of Prompt Engineering

What is Prompt Engineering?
  - Definition and Importance
  - Use Cases Across Domains
Key Concepts:
  - Tokens, Temperature, and Top-p Sampling
  - Understanding Input-Output Patterns
Introduction to Generative AI Models:
  - LLMs (GPT, Llama, etc.)
  - Types of Prompts (Instruction, Completion, Few-shot)

Session 2: Crafting Effective Prompts

Prompt Types and Techniques:
  - Zero-shot, Few-shot, Chain of Thought (CoT)
  - Structured and Unstructured Prompts
Best Practices:
  - Clarity, Specificity, and Iteration
  - Handling Ambiguities in Prompts
Hands-On Activity:
  - Writing Prompts for Different Tasks
  - Case Study: E-commerce and Support Systems

Session 3: Advanced Prompting Techniques

Strategies for Complex Tasks:
  - Prompt Chaining
  - Context Window Optimization
Incorporating Domain Knowledge:
  - Customizing Prompts for Specific Industries
Hands-On Activity:
  - Designing Prompts for Summarization, Translation, and Content Generation

Session 4: Prompt Evaluation and Iteration

Metrics for Evaluation:
  - Relevance, Fluency, and Accuracy
- Iterative Refinement of Prompts
Common Pitfalls:
  - Bias in Prompts and Outputs
  - Overfitting Prompts
Hands-On Activity:
  - Debugging and Improving Prompts

Introduction to Agentic AI

Session 1: Understanding Agentic AI

What is Agentic AI?
  - Definition and Concept
  - Differentiating Agentic AI from Traditional Models
Applications of Agentic AI:
  - Autonomous Agents in Business and Industry
  - Workflow Automation and Optimization
Building Blocks of Agentic AI:
  - Embedding Models, Retrieval Systems, and Action APIs

Session 2: Architecting Agentic Systems

Core Components:
  - Decision Trees, Feedback Loops, and Memory Modules
Interaction Models:
  - Input Interpretation, Environment Awareness
  - Multi-step Decision Making
Hands-On Activity:
  - Designing an Agent to Manage a Customer Support Workflow

Session 3: Prompting for Agentic AI

Dynamic Prompting:
  - Adapting Prompts Based on Real-time Feedback
  - Handling Multimodal Inputs
Integrating Context and History:
  - Context Window Management
  - Using Memory Efficiently
Hands-On Activity:
  - Crafting Prompts for Autonomous Agents in a Real-world Scenario

Session 4: Evaluating and Scaling Agentic AI

Evaluating Agentic Systems:
  - Metrics: Accuracy, Responsiveness, Autonomy
  - Feedback Mechanisms
Challenges and Risks:
  - Ethical Considerations
  - Bias and Safety Concerns
Scalability in Agentic AI:
  - From Prototype to Deployment
  - Case Study: Building an Autonomous Agent for a Marketing Campaign
Hands-On Activity:
  - Implementing a Simple Agent with Prompt-Based Instructions

Advanced Agentic AI with LangChain, Private GPT, and DeepSeek AI

Session 1: LangChain for Agentic AI

Introduction to LangChain:
  - Overview and Capabilities
  - How LangChain enhances Agentic AI
Building Blocks of LangChain:
  - Chains, Agents, and Memory
  - Prompt Templates, Retrieval, and Tools
Building AI Agents with LangChain:
  - Integrating APIs for real-world automation
  - Hands-on: Creating an AI agent for document analysis

Session 2: Implementing Private GPT

Understanding Private GPT:
  - Why Private GPT? Use cases in security-sensitive environments
  - Deployment options: On-premise vs. Cloud
Fine-tuning and Customizing Private GPT:
  - Running Private GPT on local infrastructure
  - Hands-on: Deploying a Private GPT instance and querying internal data

Session 3: DeepSeek AI – A New Approach to LLMs

What is DeepSeek AI?
  - Comparison with OpenAI, Llama, and Claude models
  - DeepSeek AI’s approach to reasoning and decision-making
DeepSeek AI in Enterprise Applications:
  - Hands-on: Using DeepSeek AI for knowledge retrieval and summarization
DeepSeek AI vs. LangChain:
  - Combining LangChain with DeepSeek AI for enhanced intelligence

Session 4: Combining Technologies for Enterprise AI Solutions

Integrating Private GPT, DeepSeek AI, and LangChain:
  - Best practices for AI architecture in business
  - Automating document understanding, chatbots, and workflow management
Scaling AI Agents for Production:
  - Infrastructure, security, and compliance considerations
Hands-On Final Project:
  - Implementing a Private GPT + LangChain agent for an enterprise workflow