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Date: Oct 12, 2026 – Oct 14, 2026   Location:
 

Developing Advanced Generative AI Applications

Build advanced generative AI applications with memory, multi-modal output, tool calling, and agentic foundations. Parameter-efficient finetuning covers LoRA, IA3, and prompt-tuning as cheaper...

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$3,000USD
Duration 3 days
Course Code GAI-2102
Available Formats Classroom, Virtual
Next Class: Oct 12, 2026

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Overview

Course Description

Build advanced generative AI applications with memory, multi-modal output, tool calling, and agentic foundations. Parameter-efficient finetuning covers LoRA, IA3, and prompt-tuning as cheaper alternatives to full retraining when prompt engineering alone falls short. Chatbots with memory and persistence cover message persistence across conversations, long-conversation summarization, multi-interaction memory retention, and privacy in chatbot memory. Multi-modal prompting, streaming and ensembling, and function and tool calling cover non-textual inputs, image and text generation, Stable Diffusion, cost reduction, streaming output, self-consistency, mixture methods, self-refinement, function binding, response handling, and LangChain ToolKits. Advanced use cases and prompt orchestration cover embedded agents, agent-based simulation, automated code generation, cybersecurity, EU AI Act risk categories, prompt chains and conditional routing, directed graphs, and ReAct agents built with LangGraph. Hands-on labs produce a multi-modal chatbot with memory, a ReAct agent built with LangGraph, and a worked Solving-the-Zebra-Problem example. The course is designed for software developers and data scientists with Python, introductory GenAI exposure, and GAI-2101 or equivalent RAG experience.

Skills Gained

By the end of this course, participants will be able to:

  • Build chatbots with persistent memory plus multi-turn context handling
  • Apply multi-modal prompting to generate images, text, plus combinations
  • Configure function calling that integrates LLMs with external systems
  • Compose prompt chains, conditional routing, plus directed graphs through LangGraph
  • Apply LLMs to embedded agents, simulation, plus cybersecurity use cases

Who Can Benefit

This course is designed for:

  • Software Developers
  • Data Scientists

Prerequisites

Participants should enter this course with:

  • Practical Python experience
  • GAI-1101 or equivalent
  • GAI-2101 or equivalent

Organizational Objectives

This course assists organizations to:

  • Accelerate advanced GenAI feature delivery through reusable agent and chatbot patterns
  • Reduce model-tuning cost by applying PEFT before reaching for full finetuning
  • Lower governance risk by training developers in EU-AI-Act risk categories
  • Build a working knowledge of agentic AI patterns across the development team

Software

All attendees must have a modern web browser and an Internet connection.

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Course Details

Course Details

Module 1 - Parameter-Efficient Finetuning Techniques

By the end of this module, you will be able to recognize when PEFT beats full finetuning, apply prompt-based and low-rank adaptation techniques, choose between LoRA, IA3, and prompt-tuning for a given task, and evaluate PEFT performance against the base model.

  • Parameter-efficient finetuning vs. traditional finetuning
  • Prompt-based methods of finetuning
  • Low-rank adaptation techniques
  • IA3 and other techniques
  • Selecting a PEFT technique
  • Evaluating PEFT performance
  • Hands-on Lab: Apply prompt-tuning with HuggingFace PEFT to harden one model against indirect prompt injection.

Module 2 - Chatbots with Memory and Persistence

By the end of this module, you will be able to persist chatbot conversation state, manage long conversations through summarization, retain memory across multiple interactions, extract and verify facts, and handle privacy in chatbot memory.

  • Memory and Persistence in Chatbots
  • Persisting Messages within a Conversation
  • Handling Long Conversations
  • Memory Across Multiple Interactions
  • Fact Extraction and Verification
  • Memory and Privacy
  • Hands-on Lab: Build a chatbot with persistent history that remembers facts and context across multiple sessions.

Module 3 - Multi-modal Prompting and Interaction

By the end of this module, you will be able to process non-textual inputs, prompt to generate and combine images and text, apply Stable Diffusion for image generation, and reduce multi-modal generation costs.

  • How Non-Textual Inputs are Processed
  • Prompting to Generate Images
  • Prompting with Images and Text
  • Understanding Stable Diffusion
  • Reducing Costs of Multi-modal Generation
  • Use Cases for Multi-modal Prompting
  • Hands-on Lab: Build a multi-modal agent that improves accessibility through combined image and text generation.

Module 4 - Streaming, Ensembling, and Advanced Output Techniques

By the end of this module, you will be able to stream LLM outputs, structure pipeline progression for streaming, apply self-consistency and self-refinement, and use mixture methods for ensembled output.

  • Streaming LLM Outputs
  • Streaming Pipeline Progression
  • Self-Consistency
  • Post-Processing of Generated Content
  • Mixture Methods
  • Self-Refinement
  • Hands-on Lab: Build a streaming LLM pipeline with self-consistency and self-refinement of mixed outputs.

Module 5 - Function and Tool Calling

By the end of this module, you will be able to bind functions to LLMs, handle tool-calling responses, apply error handling and retries, secure LLM function calls, and accelerate tool use with LangChain ToolKits.

  • How does Function Calling Work?
  • Binding Functions to LLMs
  • Handling LLM Responses
  • Error Handling and Retries
  • Security of LLM Function Calls
  • LangChain ToolKits
  • Hands-on Lab: Build a function-calling agent that interacts with an external environment through LangChain ToolKits.

Module 6 - Advanced Generative AI Use Cases

By the end of this module, you will be able to apply LLMs to embedded agents, agent-based simulation, automated code generation, cybersecurity, and risk management — including the EU AI Act categories of risk.

  • High-Efficiency Embedded Agents
  • Agent-based Modeling and Simulation
  • Automated Code Generation and Execution
  • Cybersecurity Applications
  • Risk Management Use Cases
  • EU AI Act Categories of Risk
  • Hands-on Lab: Build an LLM-based simulation and identify risks against the EU AI Act categories.

Module 7 - Prompt Chaining, Routing, and Directed Graphs

By the end of this module, you will be able to compose prompt chains with predetermined and generated paths, pass context between prompts, extend chains into directed graphs, apply conditional routing and cyclic graphs, and debug complex chains.

  • Prompt Chaining with Predetermined Paths
  • Passing Context between Prompts
  • Prompt Chaining with Generated Paths
  • Extending Prompt Chains with Directed Graphs
  • Conditional Routing and Cyclic Graphs
  • Debugging Complex Prompt Chains
  • Hands-on Lab: Build a ReAct agent with LangGraph that solves the Zebra Problem with generated paths.

Module 8 - Introduction to Agentic AI and Autonomous Systems

By the end of this module, you will be able to define agentic AI, differentiate it from traditional AI and RPA, articulate its benefits and challenges, and reason about its impact on key sectors and complex problem solving.

  • Defining Agentic AI and its Key Characteristics
  • Differentiating Agentic AI from Traditional AI and Robotic Process Automation
  • Understanding the benefits and challenges of Agentic AI
  • Real-world applications of Agentic AI
  • Potential Impact of Agentic AI on Key Sectors
  • Agentic AI for Solving Complex Problems
  • Hands-on Lab: Implement a simple agent in Python and exercise round-robin communication between two agents.

Module 9 - Agent Architectures and Frameworks

By the end of this module, you will be able to compare reactive, deliberative, and hybrid agent architectures, design a blend of reactive and deliberative behaviour, and choose between AutoGen, LangGraph, and CrewAI for a given application.

  • Overview of Architectures - Reactive, Deliberative, and Hybrid
  • Comparison of Architectures
  • Exploring Reactive Architectures
  • Exploring Deliberative Architectures
  • Blending Reactive and Deliberative Architectures
  • Agentic Frameworks - AutoGen, LangGraph, CrewAI, etc.
  • Hands-on Lab: Implement a reactive agent in AutoGen alongside a deliberative agent in LangGraph and compare their behaviour.

Schedule

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does Ascendient Learning deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

What does vendor-authorized training mean?

As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

Is the training too basic, or will you go deep into technology?

It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

How up-to-date are your courses and support materials?

We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

Are your instructors seasoned trainers who have deep knowledge of the training topic?

Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

Do you provide hands-on training and exercises in an actual lab environment?

Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

Will you customize the training for our company’s specific needs and goals?

We will work with you to identify training needs and areas of growth.  We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.

How do I get started with certification?

Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.

Will I get access to content after I complete a course?

You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.

How do I request a W9 for Ascendient Learning?

View our filing status and how to request a W9.

Reviews

I was very pleased with the course setup by ExitCertified and the instructor.

The class and material is good. I think some of the software needs to be updated.

The technical data in the AWS Solutions Architect course was very thorough.

this class was informative, made me think about certifying for the suse manager cert.

The tool provided to practice the course teachings is very functional and easy to use.