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AI Agents Fundamentals

AI Agents Fundamentals /Embeddings and LangChain Architecture

Embeddings and LangChain Architecture

Learn how embeddings represent meaning and where they fit inside the components and data flow of a LangChain application.

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8 videos in this course

AI Agents Fundamentals

Jump across the full chapter sequence without leaving the player.

01

Course Introduction and LLM Fundamentals

Start with the core ideas behind large language models and the concepts you will use throughout the agent-building workflow.

02

Embeddings and LangChain Architecture

Learn how embeddings represent meaning and where they fit inside the components and data flow of a LangChain application.

03

Building AI API Calls and LangChain Pipelines

Connect model API calls into reusable LangChain pipelines and understand how data moves between prompts, models, and outputs.

04

Mastering Prompt Engineering Techniques

Use clear instructions, context, examples, and output constraints to create prompts that produce more dependable results.

05

Native Prompt Reviewer

Build a prompt-review workflow that evaluates instructions, identifies weaknesses, and produces focused improvements before execution.

06

Agentic Design Pattern

Understand the observe, decide, act, and reflect loop that turns a model call into an agent capable of pursuing a goal.

07

Data Shapes

Define and validate the structured inputs and outputs that let agent components exchange information without ambiguity.

08

Tiger Cloud Architecture

Bring the course together in a cloud architecture that connects application services, agent workflows, and the data layer needed for production use.