Generative AI to AI Agent Masterclass for Software Engineers

Discover how free and plus plans access the latest ChatGPT model, with free access to GPT-3.5 and limited access to GPT-4, plus features like advanced analytics and file uploads.

Discover how GPT-3.5, GPT-4, and GPT-4o power everyday tasks through prompts, and follow a level zero to six roadmap for end-to-end SDLC, AWS, Terraform, and career-building.

Learn level four prompts by building a recursive web scraper with Puppeteer and node fetch, including data extraction, hyperlink removal, and unit test cases with technical documentation.

Explore end-to-end big data workflows on AWS, from ingestion and validation to parquet storage using S3, Glue, Lambda, EMR, and Terraform, with PySpark transformations, testing, and monitoring.

Learn how to generate images with ChatGPT-4o using DALL-E models, edit selections, and refine prompts—from simple additions like stars to detailed camera-inspired prompts for lifelike results.

Explore seven popular prompt engineering techniques: ic io, tag, tf, bab, care, rise, and chain of thought, with hands-on examples and ChatGPT demonstrations for software engineers.

Explore prompt engineering with 1000+ prompts, learn how prompt quality shapes AI outputs, and access categorized prompts for writing, coding, career prep, and more.

Explore the OpenAI playground to trial models, set system messages, tune outputs with temperature and max tokens, and craft production prompts.

Use the UI to boost productivity with design prompts, code generation, tests, and documentation. Leverage APIs to build products like chatbots, summarization, localization, and AI voice or music features.

Generate embeddings with OpenAI's text-embedding-ada-002 using a minimal Python example and embeddings.create for an input text, producing a 1530-something vector.

Explore vector databases tied to embeddings to store, retrieve, and manipulate high-dimensional vector data efficiently, enabling semantic search, text search, image search, product recommendations, and anomaly detection.

Foundation models enable versatile, cross-task capabilities through self-supervised pre-training, with few-shot or zero-shot prompting and fine-tuning for task-specific use. However, high compute costs, trustworthiness concerns, and explainability issues challenge adoption.

Explore foundation models, generative AI, and LLMs, clarify their differences, and learn when to apply each in software engineering tasks.

Define the scope and objectives of a generative ai project, choose between training from scratch or using a pre-trained foundation model, apply prompt engineering, then evaluate, deploy, and monitor drift.

Explore how fine tuning, retrieval augmented generation, and few-shot learning train generative AI models, adapting to specific tasks, leveraging external knowledge, and generalizing from limited examples.

Explore retrieval-augmented generation by combining generative models with a vector database to deliver accurate user responses. Learn the end-to-end flow, embeddings, and offline data updates that power RAG.

Explore foundation models training: pre-training on vast data builds language and reasoning foundations; fine-tuning uses domain-specific data for task alignment; continuous pre-training updates models to maintain adaptability.

This final project combines whisper, embeddings, and a GPT-3.5 turbo based AI to assist customer representatives by querying a vector database for context and generating responses.

Generate embeddings from customer call details by building a data frame, creating a reusable embeddings function, and adding an embedding column to enable search by text similarity.

Capture user interactions by recording audio and transcribing it with the Whisper model, turning customer voice into text.