Building AI Application With Large Language Model

Content

Module 0: Introduction to AI

• What is Artificial Intelligence
• Brief history of AI
• Importance and applications of AI  
• Building blocks of AI
• Type of AI  
• AI in Real-world Applications
• Healthcare applications
• Finance and trading
• Autonomous vehicles
• Natural resource management
• AI Tools and Technologies
• AI programming languages (e.g., Python)
• AI libraries and frameworks (e.g., TensorFlow, PyTorch)
• AI development environments

Module 1: Introduction to Large Language Models

• What is LLM?
• Historical evolution of LLMs
• What are the Functionality of LLM
• Real time examples of LLM
• Transform Model Architecture of LLM

Module 2: Capabilities of LLMs

• Advanced Text Generation
• Understanding Context and Nuance
• Language Translation
• Conversational AI
• Sentiment Analysis
• Code Generation and Auto-completion
• Predictive Text and Autocorrection
• Text Classification     

Module 3: Setting Up the Development Environment with LLM

• Tools and platforms for LLM development
• Basic setup for creating AI applications with LLMs

Module 4: Basic Programming with LLMs

• Introduction to programming with LLMs
• Key terms in Introduction to programming with LLMs
• Text generation, summarization with examples

Module 5: Advanced Features of LLMs

• Exploring advanced capabilities: Context retention
• Style mimicry
• Nuanced sentiment analysis
• Style transfer
• Fine-tuning and customization techniques

Module 6: Integrating LLMs in Applications

• Techniques for integrating LLMs into existing applications
• API usage and management

Module 7: Fundamentals of Finetuning

• What is Finetuning?
• Where finetuning is needed?
• How to finetune a LLM
• Compare a finetune model to a non fine tuned model
• Difference between prompt engineering and Finetuning

Module 8: Real world Use Cases

• Case Study 1: Content creation and automation

Module 9:  Real world Use Cases

• Case Study 2: Customer service chatbots

Module 10: Future Trends and Conclusion

• Emerging trends in LLMs and AI
• Discussion on the future impact of LLMs in various sectors

Module 11: Case Study: LLM Integration in Oakwood High School

• A case study about the use of a Large Language Model (LLM) in the education sector

Module 12: Best Optimization Strategies for Large Language Models

• Data Efficiency Optimization
• Model Architecture Optimization
• Training Techniques Optimization
• Resource Management Optimization
• Fine-Tuning and Generalization Optimization
• Evaluation and Testing Optimization
• Human-in-the-Loop Optimization
• Ethical Considerations Optimization
• Explainability and Interpretability Optimization
• Deployment and Scaling Optimization

Lab 1 – Compare fine-tuned vs non-finetuned models

Lab 2 – Finetune Llama 2

Lab 3 – Langchain evaluating LLM

What does an AI Application with LLM do?

This course will give the students to understand how an AI application that utilizes a Large Language Model (LLM) is designed to perform tasks that involve processing and generating human-like text.

These capabilities make LLMs highly versatile tools that can be adapted for a wide range of industries, from customer service and education to content creation and software development.

What are the domains AI Engineer work?

An AI engineer works across various domains and industries that leverage natural language processing and AI technologies. Some of the common domains where an AI engineer may work include.

• • Chatbot Development
  • Virtual Assistants
  • Customer Support
  • Content Generation
  • AI Research
  • Language Model Fine-Tuning
  • Social Network, Media, and Communication
  • SaaS industry
  • E-Commerce and Finance

Top Job Roles of AI Engineer

• • Conversational AI Developer
  • NLP Engineer
  • AI Solution Architect
  • AI Research Scientist
  • Text Embed Engineer

Salary of an AI Engineer in India.

According to recent surveys and reports, the average annual pay package for an AI engineer in India is around ₹16 lakhs per year. However, the salary may vary depending on factors such as years of experience, skills, location, and the organization you work for. An Entry level Prompt Engineer gets an offer of ₹5.6 lakhs per year. Senior professionals with a considerable amount of experience can earn up to ₹30 lakhs per annum. Some startups and large technology firms offer higher salaries to attract top talent for critical roles in conversational AI and NLP technologies.

Is it worth to pursue a career in AI engineer?

It's worth noting that the field of fine Tuning engineering is rapidly evolving, and demand for skilled engineers is expected to increase in the coming years. With this projected growth, salaries for fine tune engineers may continue to rise, making it a lucrative career choice.