GenAI real world projects in Python : Build 3 end-2-end LLM apps with LangChain, RAG, Vector DB, ChatGPT, Google Gemini
β±οΈ Length: 3.4 total hours
β 5.00/5 rating
π₯ 580 students
π January 2026 update
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- Course Overview
- This specialized program focuses on bridging the gap between theoretical artificial intelligence concepts and the practical execution of production-grade software by utilizing the latest Python frameworks and LLM orchestration tools.
- Students will embark on a journey through the complete lifecycle of generative application development, starting from environment setup and API integration to deploying functional, user-facing interfaces.
- The curriculum is structured around three cornerstone projects that reflect contemporary industry demands, ensuring that learners build a diverse portfolio of Generative AI solutions.
- By moving beyond simple prompt-response interactions, the course emphasizes the creation of “aware” applications that can interact with private datasets and external databases through sophisticated retrieval mechanisms.
- The training highlights the transition from OpenAI ecosystems to multi-provider environments, teaching students how to remain model-agnostic by integrating Google Gemini and other competitive models.
- Special attention is given to the 2026 updates, incorporating the newest features of LangChain and the most efficient methods for managing high-dimensional data in Vector Databases.
- Requirements / Prerequisites
- A foundational proficiency in Python programming is essential, particularly an understanding of lists, dictionaries, functions, and basic object-oriented programming concepts.
- A functional development environment on Windows, macOS, or Linux with Python 3.9+ installed and the ability to manage virtual environments for dependency isolation.
- Access to API keys for OpenAI and Google Cloud (Gemini), as these are necessary for the real-world execution of the projects described in the syllabus.
- Familiarity with Command Line Interfaces (CLI) for installing packages via pip and running local scripts or web servers.
- While advanced mathematics is not required, a basic conceptual understanding of how Machine Learning models process information will help in grasping embedding and vectorization concepts.
- Skills Covered / Tools Used
- LangChain Orchestration: Mastering the core components such as Chains, Memory, and Agents to build automated workflows that can reason and act based on user input.
- Retrieval Augmented Generation (RAG): Implementing advanced RAG pipelines that allow LLMs to access and synthesize information from local PDFs, text files, and live web data.
- Vector Database Management: Gaining hands-on experience with platforms like Pinecone, ChromaDB, or FAISS to store and query high-dimensional embeddings for semantic search.
- Multi-Model Integration: Learning to swap and compare outputs between GPT-4o and Google Gemini Pro to optimize for cost, speed, and accuracy within a single application.
- Prompt Engineering Architecture: Designing structured templates and system messages that guide the LLM toward consistent, high-quality, and safe outputs for business use cases.
- Frontend Deployment: Using Streamlit to transform Python backend logic into interactive, browser-based applications that stakeholders can test and use immediately.
- Document Processing: Utilizing advanced loaders and splitters to handle unstructured data, ensuring that large documents are chunked appropriately for model context windows.
- Benefits / Outcomes
- Graduates will possess a robust portfolio consisting of three end-to-end LLM applications, serving as tangible proof of their technical capabilities to potential employers or clients.
- Develop the ability to solve the “hallucination” problem in AI by grounding model responses in verifiable, proprietary data through custom-built Vector Store indexes.
- Gain the confidence to architect AI solutions that are scalable, moving from local prototypes to cloud-ready applications that follow industry best practices for security and efficiency.
- The course provides a deep competitive advantage by covering the latest 2026 updates, ensuring learners are not using deprecated libraries or outdated implementation patterns.
- Participants will move from being passive consumers of AI technology to active builders, capable of automating complex information-retrieval tasks and creative workflows.
- Enhanced understanding of the AI economics, including token usage optimization and choosing the right model size for specific project requirements to minimize operational overhead.
- PROS
- Highly Rated: Boasts a perfect 5.0/5 rating, reflecting exceptional student satisfaction and high instructional quality.
- Up-to-Date Content: The January 2026 update ensures that all code snippets and library versions are compatible with the current fast-moving AI landscape.
- Efficient Learning: At 3.4 total hours, the course is designed for busy professionals who need to gain maximum practical skill in a concentrated timeframe.
- Dual Model Mastery: Unlike many courses that focus solely on ChatGPT, this provides valuable exposure to Google Gemini, broadening the developer’s toolkit.
- CONS
- Fast-Paced Delivery: Due to the concise nature of the course, absolute beginners in Python may find the rapid progression through complex API integrations challenging without supplementary research.
Learning Tracks: English,Development,Data Science
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