Building AI Applications with Large Language Models — PickAClass
★ 3.8 (6) ⏱ 3h 📚 29 lessons 🎧 Audio version

Building AI Applications with Large Language Models

A beginner's guide to creating intelligent apps using prompt engineering, Retrieval-Augmented Generation (RAG), and AI agents.

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About this course

Ready to move beyond using AI tools and start building your own? This course provides a practical, text-based introduction to developing applications powered by Large Language Models (LLMs), guiding you from core concepts to functional projects. You'll gain the foundational knowledge to design and construct AI systems that can understand context, retrieve information, and perform tasks. This course demystifies the technology behind modern AI, giving you the skills to create your own intelligent applications from the ground up. What you'll learn: - Understand the core concepts of LLMs, text embeddings, and vector databases. - Practice effective prompt engineering techniques to reliably control model behavior and output. - Build applications using the Retrieval-Augmented Generation (RAG) pattern to answer questions based on custom data. - Learn the fundamentals of fine-tuning to adapt pre-trained models for specialized tasks. - Create autonomous AI agents that can use tools and make decisions to accomplish complex goals. - Apply popular frameworks like LangChain and libraries from Hugging Face to streamline your development process. - Configure basic user interfaces to interact with your AI applications. The curriculum begins with essential terminology and the principles of LLM operation, then moves into practical written exercises where you'll apply these concepts to build increasingly sophisticated systems. This course is designed for absolute beginners. No prior experience with AI or machine learning is necessary, though a basic familiarity with Python programming will be beneficial. Start learning to build the next generation of AI applications today.

Course contents

6 modules · 29 lessons · 3h

1 LLM Fundamentals and Architecture
Understand how language models work and set up the development environment.
6 lessons · 35 min
  1. Welcome to the World of LLMs
    An overview of the course and what it means to build AI applications today.
    5 min
  2. What is a Large Language Model?
    Explanation of core concepts: next-token prediction and training.
    6 min
  3. Tokens and Context Window
    Understanding how models read text and the limits of their short-term memory.
    6 min
  4. The Power of Embeddings
    How to transform words into numerical vectors so the machine can 'understand' them.
    6 min
  5. Exploring Hugging Face
    Using the leading library to access thousands of open-source models.
    6 min
  6. Python Environment Setup
    Installing the necessary libraries to start programming.
    6 min
2 Strategic Prompt Engineering
Learn how to communicate effectively with AI to achieve precise results.
5 lessons · 30 min
  1. Anatomy of an Effective Prompt
    Instructions, context, input, and output indicators.
    6 min
  2. Zero-shot and Few-shot Learning
    Teaching the model a task by providing examples directly within the prompt.
    6 min
  3. Chain of Thought
    Asking the model to 'think step by step' to solve logical problems.
    6 min
  4. Output Formatting
    Forcing the model to respond in JSON or other structured formats.
    6 min
  5. Mitigating Hallucinations
    Techniques to reduce fabricated responses and increase reliability.
    6 min
3 RAG and Data Management
Connecting AI to external data sources for up-to-date and private responses.
5 lessons · 30 min
  1. Why RAG is Necessary
    Overcoming the training cutoff date limitation of models.
    6 min
  2. RAG Architecture
    The workflow: Retrieval, Augmentation, and Generation.
    6 min
  3. Vector Databases: ChromaDB and Pinecone
    Storing and searching for information based on semantic meaning.
    6 min
  4. Loading Custom Documents
    Transforming PDFs and text files into chunks ready for AI.
    6 min
  5. Building a Q&A System
    Practical exercise: creating a bot that answers questions about a specific manual.
    6 min
4 Development with LangChain and UI
Using professional frameworks to orchestrate the application and create interfaces.
5 lessons · 30 min
  1. Introduction to LangChain
    Why use a framework to connect different AI components.
    6 min
  2. Creating Chains
    Chaining multiple LLM calls for complex tasks.
    6 min
  3. Memory Management
    Allowing the application to remember previous chat messages.
    6 min
  4. Interfaces with Streamlit
    Creating a web app for your AI with just a few lines of Python.
    6 min
  5. From Code to Interface
    Connecting the LangChain backend to the UI for the end user.
    6 min
5 Fine-tuning and Specialization
Understanding when and how to further train a model for specific tasks.
4 lessons · 24 min
  1. Prompt Engineering vs. Fine-tuning
    Understanding when it is necessary to retrain the model.
    6 min
  2. Dataset Preparation
    How to structure data for fine-tuning.
    6 min
  3. Adapting Pre-trained Models
    The technical process of specializing a model for a specific tone or domain.
    6 min
  4. Model Evaluation
    Measuring whether fine-tuning has actually improved performance.
    6 min
6 AI Agents and Automation
Creating systems capable of acting autonomously to solve problems.
4 lessons · 31 min
  1. What are AI Agents?
    Systems that use the LLM as a reasoning engine to take action.
    6 min
  2. Tools and Functions
    Giving the AI the ability to use calculators, APIs, or search the web.
    8 min
  3. Decision-Making Process and Loops
    How the agent plans steps to achieve a complex goal.
    8 min
  4. Final Project: The Autonomous Agent
    Building an assistant that decides which tools to use to solve a task.
    9 min
Final project

Personal Knowledge Assistant

Develop a complete application that uses a web interface to allow users to upload documents, query them via RAG, and activate an agent capable of searching the web if the information is not found in the documents.

What you'll get

  • 📜 Certificate of completion
    Add it to your LinkedIn profile
  • 💬 Personal AI tutor
    Stuck on a lesson? Ask your built-in tutor anything, any time.
  • 🎧 Audio version included
    Learn on the go — no screen needed
  • ♾️ Lifetime access
    Come back anytime, no expiry
  • 📱 Phone or computer
    Works anywhere, any device
  • 💸 14-day refund
    No questions asked
  • ⚡ Short & focused
    3h of practical content

Certificate of completion

Every course you complete on PickAClass issues a credential like this — original, with its own code, verifiable by URL, and detailed about what was actually demonstrated.

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Certificate of Mastery
This certifies that
Name Surname
has successfully demonstrated mastery of
Building AI Applications with Large Language Models
Skills demonstrated
✓
Behavioral pattern analysis
Foundational
1.2 hrs
✓
Decision-architecture frameworks
Proficient
1.4 hrs
✓
A/B test design
Proficient
1.7 hrs
✓
Behavioral copywriting
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1.9 hrs
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PickAClass — Name Surname
Building AI Applications with Large Language Models
Page 2 of 2
Performance detail
Coursework summary
Lessons completed 14 / 14
Practice questions 26 / 28
Assignments submitted 4 (avg 4.5 / 5)
Capstone project Reviewed — 4.6 / 5
Total practice 6.2 hrs
Performance benchmark
Cohort rank Top 12% of 1,625
Time to completion 11 days (median: 22)
Mastery score 91 / 100
Practice-question score 94%
Skill verification Verified Skill Path
Verify this credential
pickaclass.com/certificates/PCC-2026-X4F7-AP19
Issued under the academic standards of PickAClass. Skill levels reflect assessed performance against the course's competency rubric. This is an original credential of this platform.

Reviews (6)

Bode Lawson NG Verified learner
★ 5 · October 15, 2026

Fantastic course. The examples used were spot on and really helped solidify the concepts. My understanding has improved dramatically.

Victoria Romero EC Verified learner
★ 4 · September 17, 2026

This was a really enjoyable learning experience. The content flowed well and the practical application advice was top-notch.

Lukas Schmidt CH Verified learner
★ 4 · September 2, 2026

This course exceeded my expectations. The real-world applications discussed are incredibly useful. Great job!

Konstantinos Papageorgiou GR Verified learner
★ 3 · August 16, 2026

Hmm, I'm not sure this is for absolute beginners. It assumes a bit of prior knowledge that wasn't explicitly taught. Some examples were confusing.

Ella Lewis NZ Verified learner
★ 4 · June 27, 2026

Wow, what a great learning experience. The real-world applications discussed were so relevant. I'm already applying what I learned.

Đỗ Văn Dũng VN
★ 3 · June 21, 2026

Fantastic learning experience. The pace was perfect, and the examples really solidified the concepts. Big thumbs up!

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