Generative AI Real world Projects in Python

Discover how generative AI uses algorithms to create new content such as text, images, audio, and videos, and explore tools like ChatGPT, Gemini, GitHub Copilot, and Whisper.

Discover key genai tools and platforms, including GPT, Gemini, Claude, and Llama models, and hosting via Hugging Face and Grok, with LangChain and LangGraph for end-to-end llm apps.

Trace the evolution from machine learning to deep learning, then to the generative ai and llm era, where LLMs generate essays, code, and images, videos, and audios.

Explore how generative AI powers real-world applications to improve products and user experiences, from Google search summaries and Google Lens to YouTube thumbnails, video scripts, and AI chat support.

Identify the client problem and shape it into a clear product vision for real-world AI projects, mapping business needs to requirements and data sources with product owner and business analyst.

Stage 2 data collection explains why ai projects need data. Data scientists coordinate with product owners and analysts to identify sources from CSV, JSON, TSV, SQL, NoSQL, and vector databases.

Preprocess raw data into cleaned, model-ready input by cleaning text, using a vector database, and generating embeddings for semantic search. Incorporate image and audio preprocessing with vision models and speech-to-text.

Choose the right gen AI model by balancing foundation alignment and custom alignment, using models like Lamas, GPT, and Gemini for text, image, and audio tasks.

Explore model alignment and adaptation through prompt engineering, RAG, fine-tuning, and training with human feedback. Learn how these methods improve output quality, consistency, and domain context.

Set up a stable Jupyter notebook environment with fixed package versions using conda, install dependencies from requirements.txt, and launch a Jupyter session for real-world LLM projects.

Build an end-to-end generative ai app that analyzes a job posting url, extracts required skills, and generates a cold email to hr to win projects.

Explain the project architecture for end-to-end llm apps: extract job skills from postings, surface portfolio projects, and generate personalized cold emails via enhanced prompts across the project life cycle.

Explore the basics of prompt engineering and data pre-processing, covering four components (context, instruction, input data, output indicator) and the contrast between zero-shot and few-shot prompting with examples.

Explore how vector databases store data as vectors, use embeddings to convert text or images, and perform semantic similarity searches, contrasting them with SQL for building end-to-end GenAI apps.

Learn to build a vector store with chroma, preprocess data using pandas, and insert portfolio records into a portfolio collection, enabling similarity search for relevant portfolio links.

Activate a fixed, virtual Python environment to stabilize a JNI project, install required libraries via a requirements.txt, and launch a Jupyter notebook to build a text-to-sql converter.

Architect the project by connecting to a database, initializing the Gemini flush model, and using a long-chain SQL generator to create and execute queries from English questions, deploying a UI.

Establish a SQLite URI to connect to the database, understand relational, document-centric, and vector databases, and learn to read top ten rows from tables such as t shirt and discounts.

Learn to initialize the Gemini 2.5 flash model, configure the API key, and select from Google Gemini’s text, image, audio, and embedding models for end-to-end llm apps.

Initialize a SQL execution tool in LangChain to run SQL queries generated by language models, connecting a SQL database tool to a DB object and an executor for backend execution.

Learn how Gemini models convert a user question into a single SQL statement using defined tables and prompts to fetch stock by size and color.

Explore end-to-end generative AI workflows that generate and execute SQL queries using a Lang SQL execution tool, converting unstructured data to structured results and computing brand inventory metrics.

Load and import an image with the Python imaging library (PIL), open a file path, and view its size. Resize, rotate, crop, and apply basic filters for project use.

Use the blip vision model to extract an image caption via a processor, convert the image to tensors, and decode token IDs to a final caption.

Explore the RAG pipeline: retrieve nutrition data from the USDA database, augment the prompt with unstructured values, and generate a structured calorie report.

Explore real-time retrieval using a USDA database lookup, API key access, and Python requests to fetch food descriptions and support similarity search for better item matching.

Convert the caption to vector embeddings, then use cosine similarity against a 50-item database to surface the top five most similar food items.

Extract the nutritional information of the most similar food item, dessert pizza, by querying a USDA API and parsing nutrients like protein, carbohydrates, and calories.

Generate the final calorie report in a rag pipeline by configuring and calling a gpt-based model via grok, sending a structured prompt, and extracting nutrition data for a food item.