Generative AI for Software Engineers Training by Tonex
Generative AI for Software Engineers Training by Tonex provides a hands-on approach to integrating generative AI into applications. This course covers large language model (LLM) integration, retrieval-augmented generation, prompt engineering, and scalable AI deployment using modern tools like LangChain, vector databases, and cloud platforms. Participants will learn how to build contextual AI applications and optimize AI performance. Through real-world case studies and interactive workshops, software engineers will gain practical skills in deploying AI-driven solutions effectively. This training ensures a strong foundation in AI-powered software development, equipping professionals with the expertise needed to implement cutting-edge AI strategies in their projects.
Audience:
- Software engineers
- AI developers
- ML engineers
- Cloud architects
- Tech leads
- Product managers
Learning Objectives:
- Understand the fundamentals of generative AI in software applications
- Learn how to integrate LLMs with LangChain and vector databases
- Develop contextual AI applications using retrieval-augmented generation
- Optimize AI performance through advanced prompt engineering techniques
- Deploy AI applications efficiently using scalable cloud infrastructure
Course Modules:
Module 1: Introduction to Generative AI for Software Engineers
- Overview of generative AI and its role in software development
- Key LLM architectures and their applications
- Understanding vector databases and embeddings
- Real-world use cases of AI-powered software
- Challenges in integrating generative AI into applications
- Ethical considerations in AI-driven software solutions
Module 2: LLM Integration with LangChain and Vector Databases
- Introduction to LangChain and its framework for AI applications
- Connecting LLMs with vector databases for knowledge retrieval
- Implementing embeddings for contextual AI responses
- Enhancing AI models using retrieval-augmented generation (RAG)
- Handling large-scale AI queries efficiently
- Best practices for seamless LLM integration
Module 3: Building Contextual AI Applications
- Fundamentals of retrieval-augmented generation (RAG)
- Designing AI-driven question-answering systems
- Improving AI response relevance using contextual memory
- Handling ambiguity and improving accuracy in AI-generated content
- Implementing domain-specific knowledge retrieval
- Case study: AI-driven customer support applications
Module 4: Prompt Engineering for LLM Optimization
- Understanding the impact of prompts on AI responses
- Techniques for crafting effective AI prompts
- Optimizing AI output using structured prompts
- Handling AI biases through prompt refinement
- Experimenting with temperature and top-k/top-p sampling
- Evaluating prompt effectiveness in real-world scenarios
Module 5: Scalable AI Deployment Strategies
- Deploying AI applications using Docker and Kubernetes
- Cloud-based AI deployment on AWS, Azure, and GCP
- Ensuring security and compliance in AI deployment
- Automating AI workflows for continuous integration
- Managing AI scalability and performance tuning
- Monitoring and maintaining AI-driven applications
Module 6: Workshop and Case Study
- Hands-on workshop: Building a Q&A system with LangChain and OpenAI
- Implementing AI-driven chatbots for real-world applications
- Case study: AI integration in enterprise customer support
- Addressing deployment challenges in AI-powered applications
- Evaluating AI performance and improvement strategies
- Future trends in generative AI for software development
Enhance your expertise in generative AI with Tonex. Gain practical skills in AI integration, optimization, and deployment. Join now and lead the future of AI-powered software engineering!