Gemini and Google AI Studio: Building and Deploying AI Applications
Course Curriculum
12 modules designed to master the subject.
Module 1: Introduction to Gemini and Google AI Studio
What Gemini Models Are, Overview of Google AI Studio, Use Cases, Capabilities, and Ethics.
What Gemini Models Are: The Next Generation of Multimodal AI
A deep dive into Google's Gemini family of models. Understand their native multimodal architecture, variants (Nano, Flash, Pro, Ultra), and how they differ from traditional LLMs.
Overview of Google AI Studio: Your Hub for Gemini Development
A comprehensive guide to Google AI Studio. Learn to create projects, manage API keys, explore the prompt interface, and export code.
Use Cases Across Industries: Real-World Applications of Gemini
Explore how Gemini and multimodal AI are transforming industries from Healthcare to Finance, Coding, and Customer Service.
Capabilities and Limitations: Knowing the Boundaries
A pragmatic look at what Gemini can and cannot do. Understand hallucinations, reasoning gaps, and the trade-offs of large context windows to build reliable apps.
Ethics and Responsible AI: Building Safe Applications
AI is powerful technology that carries risk. Learn about bias, toxicity, data privacy, and the Google AI Principles governing responsible development.
Module 2: Understanding Gemini Models
Architecture, Model Sizes, Embeddings, Tokenization, and Handling Outputs.
Architecture and Design of Gemini Models
Deep dive into the architecture of Gemini. Understand how its native multimodal design differs from traditional LLMs and how it processes interleaved data.
Model Sizes and Variants: Nano, Flash, Pro, and Ultra
Choosing the right model is critical for cost and performance. Learn the detailed specs and use-cases for Gemini Nano, Flash, Pro, and Ultra.
Embeddings, Text, and Multimodal Capabilities
Understand the concept of 'Embeddings' in Gemini. Learn how text and images are converted into vectors for semantic search and classification.
Tokenization and Context Length: How Gemini Reads
Demystifying tokens. Learn how Gemini counts words, images, and videos, and what the massive 1M+ token context window enables.
Handling Model Outputs: Streaming, JSON, and Safety
Master the art of receiving data from Gemini. Learn about streaming responses for UX, forcing JSON mode for code, and handling safety interruptions.
Module 3: Google AI Studio Basics
Project Setup, Datasets, Notebook Integration, Model Registry, and Access Controls.
AI Studio Project Setup: Getting Started
Step-by-step guide to setting up your environment in Google AI Studio. Account creation, GCP linking, and billing basics.
Datasets and Storage Management
Learn how to manage data in Google AI Studio. Uploading CSVs for few-shot prompting, managing Google Drive files for long-context analysis, and handling temporary storage.
Notebook Integration: Colab and Kaggle
Seamlessly move from UI to Code. Learn how to export your AI Studio work directly to Google Colab or Kaggle notebooks for reproducible research.
Model Registry and Versioning
Manage your prompts and tuned models. Learn how to save, version, and organize your AI assets within Google AI Studio.
Access Controls and Collaboration
Work as a team. Understand API key scopes, sharing prompts, and managing permissions in a collaborative AI project.
Module 4: Prompting Gemini Models
Effective Prompting Techniques, System vs User Prompts, Few-Shot Learning, and Prompt Templates.
Effective Prompting Techniques: Speaking Gemini's Language
Master the art of prompting. Learn the core principles of clarity, specificity, and structure to get high-quality outputs from Gemini.
System vs User Prompts: Defining Persona
Learn the crucial architectural difference between System Instructions (the rules) and User Prompts (the task). How to use System Instructions to safeguard your app.
Few-Shot and Zero-Shot Learning
The most powerful technique in prompt engineering. Learn when to use Zero-Shot (asking directly) vs Few-Shot (giving examples) to solve complex tasks.
Iterative Prompt Refinement: The Loop
Prompting is not 'set and forget'. Learn the iterative loop of testing, analyzing failure cases, and refining your instructions (Chain of Thought).
Prompt Templates: coding for Reusability
Move from hardcoded strings to dynamic templates. Learn to use variables in prompts to build scalable applications.
Module 5: Fine-Tuning and Custom Models
When to Fine-Tune, Dataset Preparation, Hyperparameters, and Evaluating Models.
When Fine-Tuning is Needed: To Train or Prompt?
Fine-tuning is expensive and slow. Prompting is cheap and fast. Learn the decision framework for when to actually finetune a Gemini model.
Dataset Preparation for Tuning
Garbage in, garbage out. Learn how to format, clean, and balance your dataset for successful Gemini fine-tuning.
Parameter Adjustment and Hyperparameters
Epochs, Batch Size, and Learning Rate. Learn what these knobs do and how to set them for a Gemini tuning job.
Uploading Custom Data and Running Jobs
Walkthrough of the Tuning UI. How to upload your dataset, start the training job, and monitor its progress.
Evaluating Fine-Tuned Models
Did it work? Learn how to interpret Loss Curves and perform human evaluation to verify your fine-tuned model is better than the base model.
Module 6: Integrating Gemini Models
API Access, SDKs (Python/JS), Embedding Workflows, Multi-Model Pipelines, and Error Handling.
API Access and Authentication: Connecting to Gemini
Your first lines of code. Learn how to install the Google Generative AI libraries, secure your API keys, and make your first authenticated request.
Python and JavaScript SDKs: A Deep Dive
Master the primary tools for Gemini development. Compare the Python SDK for backend logic and the JavaScript SDK for frontend/Node.js interactions.
Embedding Workflows: Searching with Math
Implement a semantic search engine. Learn to generate embeddings for a list of documents and calculate cosine similarity to find the best match.
Multi-Model Pipelines: Chaining Logic
Combine models for complex tasks. Learn to chain a fast model (Flash) for extracting data with a smart model (Pro) for writing the final copy.
Error Handling: Retries and Rate Limits
Production code must not crash. Implement robust error handling for 429 Rate Limits, 500 Server Errors, and Safety Violations.
Module 7: Multimodal Capabilities
Text, Image, and Audio Inputs, Combining Modalities, and Multimodal Prompting.
Text, Image, and Audio Inputs: Multimodality Basics
Gemini is more than an LLM. Learn how to pass Images (PIL), Audio, and Video files into the model using the Python SDK.
Combining Modalities: Text + Image + Video
The power of mixing inputs. Learn to build advanced prompts that reference multiple images, text instructions, and video snippets simultaneously.
Multimodal Prompting: Asking the Right Questions
Prompts for images are different than prompts for text. Learn techniques like Spatial Referencing and Visual Reasoning.
Use Cases and Applications: Multimodal in the Wild
Real-world examples of multimodal AI. From automated insurance claims (Photos) to meeting minutes (Audio) and sports analytics (Video).
Module 8: RAG with Gemini
Vector Stores, Chunking, Querying, Retrieval, and Validating Responses.
Why RAG is Important: The Knowledge Bridge
Understand the 'Knowledge Cutoff' problem. Learn how RAG (Retrieval Augmented Generation) connects Gemini to your private, real-time data.
Creating Vector Stores: Your Database of Meaning
Learn how to store embeddings efficiently. Introduction to ChromaDB and Pinecone for fast similarity search.
Chunking Large Documents: slicing Data
You can't embed a whole book as one vector. Learn strategies for splitting text into meaningful chunks (Recursive, Semantic) for better retrieval.
Querying and Retrieval: The RAG Loop
Bring it all together. Build the full loop: Query -> Embed -> Search Vector DB -> Construct Prompt -> Generate Answer.
Validating Responses: Anti-Hallucination
Trust but verify. Techniques to ensure Gemini isn't making things up, including citation extraction and 'I don't know' fallbacks.
Module 9: Automation and Workflows in AI Studio
Scheduling Jobs, Notebook Automation, Multi-Step Workflows, and Monitoring.
Scheduling Jobs and Pipelines: AI on the Clock
AI isn't just for chat. Learn how to schedule periodic AI jobs (like nightly report summarization) using Cloud Scheduler or simple cron scripts.
Notebook Automation: From Research to Production
Stop running cells manually. Learn to automate Jupyter Notebooks using tools like Papermill or converting them to robust Python scripts.
Multi-Step Model Workflows: The Assembly Line
Break complex tasks into a DAG (Directed Acyclic Graph). Step 1: Ingest. Step 2: Summarize. Step 3: Critique. Step 4: Finalize.
Data Preprocessing and Postprocessing
Clean your inputs and sanitize your outputs. Using Regex and validation libraries to ensure Gemini connects cleanly to your databases.
Monitoring and Logging: Full Observability
You can't fix what you can't see. Learn to log prompts, responses, latency, and costs to tools like BigQuery or LangSmith.
Module 10: Security, Privacy, and Compliance
Data Privacy, Access Management, Model Output Governance, and Compliance.
Data Privacy Considerations: Your Data, Your Rules
Understand where your data goes. The difference between Consumer (Free) and Enterprise (Paid) data usage policies in the Gemini ecosystem.
Access Management: IAM and Least Privilege
Secure your AI infrastructure. Learn to use Google Cloud IAM (Identity and Access Management) to restrict who can call models or view tuning data.
Model Output Governance: Watermarking and Copyright
Dealing with the legal grey areas. Understanding SynthID (Watermarking) and managing copyright risks in generated content.
Safe Sharing and Collaboration
collaborating without leaking secrets. Best practices for sharing prompts, logs, and datasets within your team.
Compliance Best Practices: SOC2, HIPAA, and GDPR
Navigating the alphabet soup of regulation. How to configure Gemini (via Vertex AI) to meet strict industry standards.
Module 11: Deployment and Production
Deploying Models as APIs, Hosting, Version Management, Scaling, and Monitoring.
Deploying AI Studio Models as APIs
Turn your Python script into a Microservice. How to wrap Gemini in FastAPI to serve predictions to your frontend.
Hosting for Applications and Services
Where does the code live? Deploying your AI backend to Google Cloud Run, Vercel, or AWS Lambda.
Version Management and Rollbacks
Prompts break. Models change. Learn how to manage versions and perform Blue/Green deployments to prevent outages.
Scaling Models: Quotas and Performance
Going viral? Learn how to handle thousands of users, request quota increases, and optimize throughput.
Monitoring Performance and Costs
The bill comes due. Learn to calculate token costs, identify expensive users, and optimize your spending.
Capstone Project: AI-Powered Application with Gemini
Build and Deploy a Gemini-Powered App Using Google AI Studio.
Course Overview
Format
Self-paced reading
Duration
Approx 6-8 hours
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