Course Overview

Transform how you automate tasks, build intelligent systems, and create multi-step workflows — all powered by the ChatGPT API.

This course is designed to help you move beyond basic prompting and into the powerful world of system design using large language models. Whether you’re building customer service chatbots, internal automation tools, or sophisticated decision-making engines — this course will equip you with the practical, production-grade skills to build, evaluate, and deploy multi-step LLM-powered systems with ease.

You’ll gain hands-on experience in prompt chaining, chain-of-thought reasoning, input/output evaluation, and system safety design — all using Python and the ChatGPT API. With a focus on clarity, efficiency, and real-world application, this course is your launchpad into advanced AI engineering.

This course is ideal for

Python developers who want to integrate LLMs into their workflows

Python developers who want to integrate LLMs into their workflows

Python developers who want to integrate LLMs into their workflows

Aspiring prompt engineers and automation specialists

Aspiring prompt engineers and automation specialists

Aspiring prompt engineers and automation specialists

Product managers and builders looking to understand how to scale AI-powered decision logic

Product managers and builders looking to understand how to scale AI-powered decision logic

Product managers and builders looking to understand how to scale AI-powered decision logic

Intermediate and advanced learners eager to explore multi-step system design using ChatGPT

Intermediate and advanced learners eager to explore multi-step system design using ChatGPT

Intermediate and advanced learners eager to explore multi-step system design using ChatGPT

Beginner-Friendly: Requires only a basic understanding of Python.

Beginner-Friendly: Requires only a basic understanding of Python.

Beginner-Friendly: Requires only a basic understanding of Python.

What You'll Learn

Design intelligent multi-step systems using the ChatGPT API

Design intelligent multi-step systems using the ChatGPT API

Design intelligent multi-step systems using the ChatGPT API

Split complex problems into sequential subtasks using advanced prompt engineering

Split complex problems into sequential subtasks using advanced prompt engineering

Split complex problems into sequential subtasks using advanced prompt engineering

Chain prompts together where the output of one becomes the input of the next

Chain prompts together where the output of one becomes the input of the next

Chain prompts together where the output of one becomes the input of the next

Integrate Python logic to interact dynamically with language model outputs

Integrate Python logic to interact dynamically with language model outputs

Integrate Python logic to interact dynamically with language model outputs

Build and evaluate chatbot systems for safety, accuracy, and relevance

Build and evaluate chatbot systems for safety, accuracy, and relevance

Build and evaluate chatbot systems for safety, accuracy, and relevance

Apply moderation filters, classification models, and thought reasoning chains

Apply moderation filters, classification models, and thought reasoning chains

Apply moderation filters, classification models, and thought reasoning chains

Key Course Highlights

11 Expert-Curated Lessons

11 Expert-Curated Lessons

11 Expert-Curated Lessons

9 Live Code Examples

9 Live Code Examples

9 Live Code Examples

Hands-on System Design

Hands-on System Design

Hands-on System Design

Use Cases in Customer Support, Content Filtering, and More

Use Cases in Customer Support, Content Filtering, and More

Use Cases in Customer Support, Content Filtering, and More

Quizzes + Summary for Recap

Quizzes + Summary for Recap

Quizzes + Summary for Recap

Approx. 3.5–4 hours Total Duration

Approx. 3.5–4 hours Total Duration

Approx. 3.5–4 hours Total Duration

Course Curriculum

Lesson 1 - Introduction

Lesson 1 - Introduction

Lesson 1 - Introduction

Overview of systems powered by LLMs

Overview of systems powered by LLMs

Overview of systems powered by LLMs

Overview of systems powered by LLMs

Real-world use cases for multi-step AI automation

Real-world use cases for multi-step AI automation

Real-world use cases for multi-step AI automation

Real-world use cases for multi-step AI automation

Lesson 2 - LLM Architecture, Tokens & Chat Formats

Lesson 2 - LLM Architecture, Tokens & Chat Formats

Lesson 2 - LLM Architecture, Tokens & Chat Formats

Understanding token usage, prompt length, and system message structuring

Understanding token usage, prompt length, and system message structuring

Understanding token usage, prompt length, and system message structuring

Understanding token usage, prompt length, and system message structuring

Code examples: Building your first ChatGPT-powered interaction

Code examples: Building your first ChatGPT-powered interaction

Code examples: Building your first ChatGPT-powered interaction

Code examples: Building your first ChatGPT-powered interaction

Lesson 3 - Classification with LLMs

Lesson 3 - Classification with LLMs

Lesson 3 - Classification with LLMs

Using ChatGPT to classify user intent

Using ChatGPT to classify user intent

Using ChatGPT to classify user intent

Using ChatGPT to classify user intent

eploying custom workflows based on classification

eploying custom workflows based on classification

eploying custom workflows based on classification

eploying custom workflows based on classification

Lesson 4 - Moderation and Safety Evaluation

Lesson 4 - Moderation and Safety Evaluation

Lesson 4 - Moderation and Safety Evaluation

Implementing filters to flag unsafe or harmful inputs

Implementing filters to flag unsafe or harmful inputs

Implementing filters to flag unsafe or harmful inputs

Setting moderation boundaries for real-world deployment

Setting moderation boundaries for real-world deployment

Setting moderation boundaries for real-world deployment

Setting moderation boundaries for real-world deployment

Lesson 5 - Chain-of-Thought Reasoning

Lesson 5 - Chain-of-Thought Reasoning

Lesson 5 - Chain-of-Thought Reasoning

Learn to guide the model step-by-step through logical problems

Learn to guide the model step-by-step through logical problems

Learn to guide the model step-by-step through logical problems

Building systems with intermediate reasoning paths

Building systems with intermediate reasoning paths

Building systems with intermediate reasoning paths

Building systems with intermediate reasoning paths

Lesson 6 - Prompt Chaining

Lesson 6 - Prompt Chaining

Lesson 6 - Prompt Chaining

Build a pipeline of multiple prompt-response pairs

Build a pipeline of multiple prompt-response pairs

Build a pipeline of multiple prompt-response pairs

Dynamically process complex logic in layered prompts

Dynamically process complex logic in layered prompts

Dynamically process complex logic in layered prompts

Dynamically process complex logic in layered prompts

esson 7 - Output Checking

esson 7 - Output Checking

esson 7 - Output Checking

Build guardrails and sanity checks for outputs

Build guardrails and sanity checks for outputs

Build guardrails and sanity checks for outputs

Use Python to interpret and act on model completions

Use Python to interpret and act on model completions

Use Python to interpret and act on model completions

Use Python to interpret and act on model completions

Lessons 8–1 - Full System Evaluation

Lessons 8–1 - Full System Evaluation

Lessons 8–1 - Full System Evaluation

Apply all previous techniques to evaluate final chatbot performance

Apply all previous techniques to evaluate final chatbot performance

Apply all previous techniques to evaluate final chatbot performance

Deep-dive into user response analysis, system tweaking, and scoring accuracy

Deep-dive into user response analysis, system tweaking, and scoring accuracy

Deep-dive into user response analysis, system tweaking, and scoring accuracy

Deep-dive into user response analysis, system tweaking, and scoring accuracy

Lesson 11 - Summary + Final Project

Lesson 11 - Summary + Final Project

Lesson 11 - Summary + Final Project

Bring it all together: Build a complete customer support chatbot that uses multi-step logic, safety checks, and classification

Bring it all together: Build a complete customer support chatbot that uses multi-step logic, safety checks, and classification

Bring it all together: Build a complete customer support chatbot that uses multi-step logic, safety checks, and classification

Wrap-up and final best practices

Wrap-up and final best practices

Wrap-up and final best practices

Wrap-up and final best practices

Course Duration

Course Duration

Course Duration

11 Lessons | 3.5–4 hours

11 Lessons | 3.5–4 hours

11 Lessons | 3.5–4 hours

ultiple Projects and Code Labs Included

ultiple Projects and Code Labs Included

ultiple Projects and Code Labs Included

Why Take This Course?

Move Beyond Basic Prompting: Learn how to design systems, not just use LLMs.

Move Beyond Basic Prompting: Learn how to design systems, not just use LLMs.

Move Beyond Basic Prompting: Learn how to design systems, not just use LLMs.

Hands-On & Practical: Every concept is immediately applied via code examples and real-world scenarios.

Hands-On & Practical: Every concept is immediately applied via code examples and real-world scenarios.

Hands-On & Practical: Every concept is immediately applied via code examples and real-world scenarios.

Build With Confidence: Learn how to evaluate output quality, moderate safely, and improve LLM reliability.

Build With Confidence: Learn how to evaluate output quality, moderate safely, and improve LLM reliability.

Build With Confidence: Learn how to evaluate output quality, moderate safely, and improve LLM reliability.

Career-Boosting: These are real skills in demand in AI product teams, MLOps, and prompt engineering roles.

Career-Boosting: These are real skills in demand in AI product teams, MLOps, and prompt engineering roles.

Career-Boosting: These are real skills in demand in AI product teams, MLOps, and prompt engineering roles.

Prerequisites

Basic Python knowledge required

Basic Python knowledge required

Basic Python knowledge required

No prior experience with APIs or LLMs needed

No prior experience with APIs or LLMs needed

No prior experience with APIs or LLMs needed

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Deepberg.ai © 2025 All rights reserved.

Start Your AI & Cloud Journey

Build real skills with practical, beginner-friendly courses — designed to help you break into tech, no matter your background.

Browse Courses

Deepberg.ai © 2025 All rights reserved.

Start Your AI & Cloud Journey

Build real skills with practical, beginner-friendly courses — designed to help you break into tech, no matter your background.

Browse Courses

Deepberg.ai © 2025 All rights reserved.