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Zero To Mastery

AI Engineering: Building AI Applications (LangChain, LLM APIs + more)

via Zero To Mastery Path

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Overview

Learn to build AI applications using LLM APIs and cutting edge tools including LangChain, LangSmith, and LangGraph. This is developer training for the new era of programming.
  • Use OpenAI & Gemini APIs to build real-world AI applications
  • Craft effective prompts using proven engineering techniques
  • Build chatbots, voice apps, and image generation tools
  • Use LangChain to create agents that use memory and tools
  • Work with embeddings & RAG for smarter search and Q&A
  • Build multi-step agents with LangGraph and LangSmith
  • Analyze, debug, and improve LLM-powered apps
  • Build a full-featured AI Research Assistant from scratch

Syllabus

  •   Section 00 - Introduction
    • AI Engineering Bootcamp: Building AI Applications
    • Exercise: Meet Your Classmates and Instructor
    • ZTM Plugin + Understanding Your Video Player
    • A Note on Course Prerequisites
    • Course Resources
    • Set Your Learning Streak Goal
  •   Section 01 - Virtual Environments in Jupyter Notebook
    • Using Jupyter Notebook
    • Using Virtual Environments (venv)
  •   Section 02 - Essential Python Libraries for AI: Requests & Pandas
    • Getting Started with the requests and httpx Libraries in Python
    • Handling HTTP Errors
    • Managing HTTP Authentication and Headers (OpenAI API)
    • Setting Up the Environment: Jupyter Notebook and Pandas
    • Introduction to Pandas: Series and DataFrames
    • Importing and Exporting Data: Working with CSV Files
    • Exporting Data to Different Formats: Excel, JSON, SQL, YAML
    • Modifying Data: Adding and Dropping Columns and Rows
    • Accessing Data: Using df.iloc[] and df.loc[]
    • Sampling and Previewing Data: Using df.sample() and df.head()
    • Filtering Data: Masks and pandas.Series.between()
    • Sorting Data: Understanding Pandas Sorting Methods
    • Handling Missing Data
    • Aggregations and Grouping Data
    • Project: Analyzing Website Traffic Data
    • Time Series Data Manipulation in Pandas
    • Unlimited Updates
  •   Section 03 - Introduction to LLMs, APIs, and AI Libraries
    • Foundations of LLMs and Generative AI
    • Tokens, Context Windows and Cost
    • Exploring LLM APIs: AI as a Service
    • OpenAI Playground, Google AI Studio, and Anthropic Workbench
    • Challenges and Limitations of LLMs
    • The State of AI: Present and Future – The Good and the Bad
    • Implement a New Life System
  •   Section 04 - Deep Dive into LLMs
    • Pretraining Data (Internet)
    • Tokenization
    • Training the Neural Network
    • Post-Training: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL)
    • Reinforcement Learning (RL)
    • Becoming Better than Humans: AGI and ASI with RL
    • Reinforcement Learning with Human Feedback (RLHF)
    • How to Deal With Hallucinations
    • Using Tools: Internet Search, Interpreter, and Deep Search
    • Big Ideas Recap (Core Summary)
  •   Section 05 - Diving into OpenAI API with Python
    • Authenticating to OpenAI using Python Dotenv
    • Chat Completions Endpoint
    • Developer Message
    • Streaming API Responses
    • Using Local Base64 Images as Input
    • Using Online Images as Input
    • Chat Completion API Parameters: Temperature and Seed
    • Chat Completion API Parameters: Top P, Max_Tokens, Penalties
    • Diving into OpenAI’s Reasoning Models (o1 and o3)
    • Best Practices for Prompting Reasoning Models
    • Transcriptions with Whisper
    • Translations with Whisper
    • Text-to-Speech (TTS) API
    • Generating Original Images Using the DALL-E 3
    • Creating Variations of Images with DALL-E
    • Editing Images with DALL-E
    • Course Check-In
  •   Section 06 - Prompt Engineering for Generative AI
    • Intro to Prompt Engineering
    • Tactic 1: Position Instruction Clearly with Delimiters
    • Tactic 2: Provide Detailed Instructions for the Context
    • Tactic 3: Use the Rich Text Format (RTF)
    • Tactic 4: Few Shot Prompting
    • Tactic 5: Specify the Steps Required to Complete a Task
    • Tactic 6: Give Models Time to Think
    • Other Tactics and Principles for Better Prompting
    • Avoid Hallucinations Using Guarding
    • Summary
  •   Section 07 - OpenAI API Project: Building a Healthy Daily Meal Plan
    • Project Introduction
    • Creating a Daily Meal Plan Using OpenAI API
    • Creating the Prompt
    • Running the Program
    • Generating Original Images for the Recipes using DALL-E
    • Narrate the Meals using the Text-to-Speech Model
  •   Section 08 - Diving into Google’s Gemini API
    • Setting Up the Python SDK and Authenticating for Gemini API
    • Generating Text From Text Prompts
    • Streaming Gemini Responses
    • Generating Text From Images
    • Gemini API Generation Parameters: Controlling How the Model Generates Responses
    • Gemini API Generation Parameters Explained
    • Building Chat Conversations
    • Project: Building a Conversational Agent Using Gemini Pro
    • System Instructions
    • The File API: Prompting with Media Files
    • Tokens
    • Prompting with Audio
  •   Section 09 - Gemini API Project: Talking With an Image
    • Project Requirements
    • Building the Application
    • Testing the Application
    • Streamlit: Transform Your Jupyter Notebooks into Interactive Web Apps
    • Creating the Web App Layout With Streamlit
    • Saving and Displaying the History Using the Streamlit Session State
    • Exercise: Imposter Syndrome
  •   Section 10 - Gemini API Project: Building an AI-Powered Image Renaming Tool
    • Project Introduction
    • Getting Images Using a Generator
    • Renaming Images Using Gemini
  •   Section 11 - Diving into LangChain
    • LangChain Demo
    • Introduction to LangChain
    • Working with the OpenAI Models
    • Caching LLM Responses
    • LLM Streaming
    • Prompt Templates
    • ChatPromptTemplate
    • Understanding Chains
    • Installing the Python Libraries for Gemini and Authenticating to Gemini
    • Integrating Gemini with LangChain
    • Using a System Prompt and Enabling Streaming
    • Multimodal AI With Gemini
    • LangChain Tools: DuckDuckGo and Wikipedia
    • Creating a React Agent
    • Testing the React Agent
  •   Section 12 - Diving into Embeddings
    • Intro to OpenAI's Text Embeddings
    • Generating Simple Embeddings
    • Embedding the Dataset for Similarity Searches
    • Estimating Embedding Costs With Tiktoken
    • Performing Semantic Searches
  •   Section 13 - RAG Project: Q&A Application on Your Private Documents (Pinecone and Chroma)
    • Project Introduction
    • Loading Your Custom (Private) PDF Documents
    • Loading Different Document Formats
    • Public and Private Service Loaders
    • Chunking Strategies and Splitting the Documents
    • Intro to Vector Stores and Authenticating to Pinecone
    • Working with Pinecone Indexes
    • Working with Vectors
    • Pinecone Namespaces
    • Embedding and Uploading to a Vector Database (Pinecone)
    • Asking and Getting Answers
    • Using Chroma as a Vector DB
    • Adding Memory to the RAG System (Chat History)
    • Using a Custom Prompt
  •   Section 14 - Building a Simple ReAct Agent from Scratch
    • Introduction to Agents and ReAct
    • Creating the Agent Class
    • Creating the ReAct Prompt
    • Creating the Tools
    • Testing the Agent
    • Automating the Agent
  •   Section 15 - Diving into LangGraph
    • LangGraph Concepts and Core Components
    • Building a Chatbot
    • Visualizing the Graph
    • Running the Chatbot
    • Tavily AI
    • Enhancing the ChatBot with Tools
    • Adding Memory to the Chatbot
  •   Section 16 - Project: Tweet Generator (Reflection)
    • Intro to Reflection
    • Generate
    • Reflect and Repeat
    • Define the Graph - Part 1
    • Define the Graph - Part 2
    • Running the App
  •   Section 17 - LangSmith: Platform for Building Production-grade LLM Apps
    • Intro to LangSmith
    • Setting Up LangSmith
    • Tracing with LangSmith
    • Tracing the Reflective Agentic App
  •   Section 18 - Project: Essay Writer (Reflexion)
    • Project Overview
    • Defining the Agent State and the Prompts
    • Implementing Agents and Nodes
    • Defining the Conditional Edge
    • Defining the Graph
    • Running the App
    • Tracing the App with LangSmith
  •   Section 19 - Master Project: Build a Research Agent with LangGraph, GPT-4o, RAG, Pinecone, ArXiv, and Google SerpAPI
    • Note
    • Application Overview
    • Extracting Data from ArXiv with Pandas
    • Downloading Research Papers
    • Loading, Splitting and Expanding Data
    • Building a Knowledge Base for RAG
    • Creating a Pinecone Index
    • Loading the Knowledge Base and Deploying to Pinecone
    • Developing Custom Tools
    • Implementing the ArXiv Fetch Tool
    • Unlocking Web Search with Google SerpAPI
    • Building Google SerpAPI Tools
    • Creating RAG Tools
    • Implementing the Final Answer Generation Tool
    • 06_14 Initializing the Oracle LLM
    • Testing the Ecosystem
    • Building a Decision-Making Pipeline
    • Defining the Agent State
    • Defining the Graph
    • Generating Reports
    • Building the Final Research Report
    • Concluding the Project
  •   Appendix - Working with Python Modules
    • Understanding Python Modules
    • The OS Module
    • Advanced Import Techniques and Best Practices
    • Using __name__ == '__main__' for Modular and Reusable Code
    • Mastering Python Package Management with pip
    • Let's Have Some Fun (+ More Resources)
  •   Where To Go From Here?
    • Thank You!
    • Review This Course!
    • Become An Alumni
    • Learning Guideline
    • ZTM Events Every Month
    • LinkedIn Endorsements

Taught by

Andrei Dumitrescu

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