AI

This document covers the complete AI application development journey, from ideation and model selection through building with RAG and fine-tuning to production deployment with MLOps best practices.

This document introduces LangChain, an open-source Python framework for developing LLM applications, exploring its benefits, practical uses, and integration capabilities with various data types.

This document explores Hugging Face, an open-source AI platform, its model hub, datasets, Spaces, and practical steps to build and customize AI apps using shared code and APIs.

Methods for retrieving data from various sources including CSV and JSON files with practical considerations using Python and Pandas

This document explores the quasi-religious nature of OpenAI's AGI mission examining how the company operates more like a belief system than a scientific endeavour with competing factions of believers and doomers.

This document introduces Python as a programming language for data science and AI, highlighting its community support, rich ecosystem, and powerful libraries for data analysis, machine learning, and deep learning.

This document demonstrates the use of AI to generate comprehensive test cases for software modules, with examples of prompt engineering for user registration validation scenarios.

This document explores generative AI applications in software testing including machine learning, NLP, and intelligent automation techniques for improved test efficiency and coverage.

This document explores the integration of AI tools in software development security, covering automated code reviews, threat detection, machine learning applications, and preventive cybersecurity measures.

This document provides an overview of AI integration in CI/CD pipelines focusing on automated testing, code optimization, intelligent release orchestration, and AI-enabled DevOps tools.

This document summarizes the generative AI module, covering key concepts including AI fundamentals, LLMs, NLP, tokens, practical applications, and tools for software development.

This document provides a comprehensive guide to using prompts in the Software Development Life Cycle, covering prompt engineering, best practices, AI integration, and optimization strategies for development workflows.

This document provides a guide to managing legacy code using AI tools covering code analysis, modernization, refactoring, documentation, and migration strategies for legacy systems.

This document provides an overview of generative AI and design tools for software development, covering popular platforms, features, integrations, and best practices for enhanced productivity.

This document provides a comprehensive guide to creating design diagrams using AI tools, covering system architecture, UML diagrams, flowcharts, and visual documentation for software development projects.

This document provides a guide to static site development using AI tools covering site generators, content management, deployment strategies, and optimization techniques for modern web development.

This document presents a practical assignment on designing a relational database using AI tools, covering schema creation, normalization, query optimization, and best practices for relational database development.

This document provides guidance on leveraging AI for software development best practices, design patterns, code review, optimization, and architectural recommendations to improve development workflows.

This document provides a comprehensive guide to tokens in generative AI covering tokenization, text processing, input limits, token pricing, and optimization strategies for AI models.

This document introduces the fundamentals of Natural Language Processing including text analysis, language understanding, machine translation sentiment analysis, and NLP applications in software development.