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Date: Sep 8, 2026 – Sep 10, 2026   Location:
 

Building Generative AI Applications

Build production generative AI applications with hosted LLMs, LangChain, and agentic patterns. The course covers GenAI foundations, prompt engineering with CLEAR and RCTO, hosted-API chatbot...

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$3,000USD
Duration 3 days
Course Code GAI-1101
Available Formats Classroom, Virtual
Next Class: Sep 8, 2026

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Overview

Course Description

Build production generative AI applications with hosted LLMs, LangChain, and agentic patterns. The course covers GenAI foundations, prompt engineering with CLEAR and RCTO, hosted-API chatbot construction, LangChain primitives, RAG, and evaluation through metrics, custom criteria, and tools including DeepEval and G-Eval. Production concerns cover deployment and scaling tradeoffs across cloud and on-premise, latency and throughput management, monitoring with alerts and dashboards, and debugging strategies that survive non-determinism. Agentic AI with LangGraph covers agentic patterns, multi-agent workflows, and error handling. Hands-on labs produce a chatbot, an evaluated LangChain pipeline, a deployed and monitored GenAI service, and a working agentic router. The course is designed for software developers and data scientists with Python and introductory generative-AI experience.

Skills Gained

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

  • Build conversational and content-generation features against hosted LLM APIs
  • Compose LangChain pipelines for prompt-templated, structured, and RAG-based applications
  • Evaluate LLM applications using metrics, custom criteria, and DeepEval/G-Eval
  • Configure deployment, scaling, and monitoring strategies for GenAI in production
  • Apply LangGraph patterns to build basic agentic LLM applications

Who Can Benefit

This course is designed for:

  • Software Developers
  • Data Scientists & Analysts

Prerequisites

Participants should enter this course with:

  • Familiarity using Generative AI
  • Practical Python experience

Organizational Objectives

This course assists organizations to:

  • Accelerate GenAI feature delivery through reusable LangChain pipelines and prompt templates
  • Reduce production incidents by codifying monitoring metrics, alert thresholds, and rollback procedures
  • Lower agent error rates through evaluation pipelines that catch regressions before deploy
  • Build a working knowledge of agentic AI patterns across the development team in three days

Software

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

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Course Details

Course Details

Module 1 - Understanding Generative AI

By the end of this module, you will be able to differentiate generative AI from traditional AI, describe how foundation models produce text and other modalities, and recognize the model settings and parameters that influence output quality.

  • Defining intelligence, artificial intelligence, and generative AI
  • Differentiating generative AI from traditional AI
  • How generative AI works (tokenization, embeddings, decoding)
  • Benefits and challenges of generative AI
  • Popular GenAI models and frameworks
  • LLM settings and parameters; intro to prompt engineering, RAG, and agentic AI
  • Hands-on Lab: Explore tokenization, embeddings, and core LLM settings hands-on without writing any code.

Module 2 - Basics of Prompt Engineering

By the end of this module, you will be able to write structured prompts using the CLEAR and RCTO/RTF frameworks, iterate prompts through a deliberate loop, and decompose tasks into sequenced prompts with human checkpoints between stages.

  • CLEAR framework
  • RCTO / RTF structure
  • Adaptive iteration loop
  • Workflow decomposition (analyze/transform/write)
  • Human checkpoints between stages
  • Prompt-vs-workflow-vs-tool decision
  • Hands-on Lab: Author and iterate one effective ChatGPT prompt that uses CLEAR and at least one n-shot variant for a workplace task.

Module 3 - Building Simple LLM-Based Applications

By the end of this module, you will be able to call LLMs through hosted APIs, structure prompts with prompt templates, build a conversational completion loop, and choose between closed-weight and open-weight models for cost and latency tradeoffs.

  • Application design building blocks
  • Accessing LLMs via APIs
  • Prompt templates
  • Conversational model of completion
  • Closed-weight vs. open-weight models
  • Batch APIs for cost control
  • Hands-on Lab: Build a vanilla chatbot against the OpenAI API using a prompt template and a conversational completion loop.

Module 4 - Accelerating Development with LangChain

By the end of this module, you will be able to compose LangChain primitives into a working application, produce structured output through function calling, and assemble RAG pipelines from loaders, splitters, parsers, and a vector retriever.

  • LangChain terminology and conceptual overview
  • LLM chains and prompt templates
  • Loaders, splitters, and parsers
  • Retrieval Augmented Generation (RAG)
  • Structured output and function calling
  • Composing chains end-to-end for production tasks
  • Hands-on Lab: Build a LangChain pipeline that returns structured output from a prompt-chained sequence of calls.

Module 5 - Basics of Evaluating Generative AI Applications

By the end of this module, you will be able to apply automated metrics to GenAI output, distinguish GenAI evaluation from conventional software testing, interpret evaluation results, and define custom evaluation metrics for application-specific quality.

  • Generative AI evaluation vs. software evaluation
  • Metrics for evaluating generative AI
  • Interpreting evaluation results
  • Advanced metrics and techniques for generative AI
  • Custom evaluation metrics
  • Hands-on Lab: Use DeepEval and G-Eval to evaluate one LLM application against built-in and custom criteria.

Module 6 - Deploying and Scaling Generative AI Applications

By the end of this module, you will be able to recognize the unique deployment challenges generative AI applications introduce, manage latency and throughput at scale, choose between cloud and on-premise deployments, and operate continuous-deployment pipelines with monitoring and auto-scaling.

  • Unique deployment and scaling challenges for generative AI
  • Managing latency and throughput
  • Ensuring data privacy and security
  • Cloud vs. on-premise deployment tradeoffs
  • Continuous deployment pipelines for GenAI
  • Monitoring and auto-scaling GenAI applications
  • Hands-on Lab: Apply scaling laws and a batching/caching strategy to optimize one GenAI deployment.

Module 7 - Basics of Generative AI Monitoring

By the end of this module, you will be able to distinguish ongoing monitoring from offline evaluation, choose key monitoring metrics for an LLM application in production, instrument an alert and log pipeline, and verify the monitoring system end-to-end.

  • Differences Between Evaluation and Monitoring
  • Identifying Key Monitoring Metrics
  • Understanding the Monitoring Workflow
  • Alerts, Logs, and Monitoring Verification
  • Setting Up a Monitoring System
  • Hands-on Lab: Stand up a monitoring system for one GenAI application with key metrics, alert thresholds, and verification.

Module 8 - Debugging and Testing Generative AI Applications

By the end of this module, you will be able to instrument GenAI applications for debugging, write tests that survive non-determinism, apply debugging and testing tools and strategies appropriate to LLM systems, and integrate them into CI/CD pipelines.

  • Debugging generative AI applications
  • Testing generative AI applications
  • Debugging and testing tools
  • Debugging and testing strategies
  • Continuous integration and deployment for GenAI
  • Hands-on Lab: Debug a chatbot application and add a test harness covering the failure modes you uncovered.

Module 9 - Basics of Agentic AI with LangGraph

By the end of this module, you will be able to build agentic LLM applications with LangGraph, recognize common agentic patterns, coordinate multi-agent workflows, and handle errors that arise when agents call tools and each other.

  • LangGraph concepts and principles
  • Common agentic patterns
  • Multi-agent workflows
  • Agent communication and coordination
  • Error handling in agentic systems
  • Scaling agentic AI applications
  • Hands-on Lab: Build an agentic router and a ReAct agent with LangGraph that share a tool and coordinate via a shared state graph.

Module 10 - Generative AI Use Cases and Ethics

By the end of this module, you will be able to identify high-value generative AI use cases inside your work context, recognize the ethical considerations that govern responsible use, and reason about AI-related risk through real case studies.

  • Real-world applications of generative AI
  • Ethical considerations in generative AI
  • Responsible use of generative AI
  • Case studies and examples
  • Hands-on Lab: Identify candidate GenAI use cases in your team’s workflow and spot the ethical issues each one raises.

Schedule

2 options available

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 thought the course was informative and the tools to go over the material were very nice.

Very good company. I've done technical trainings at their facility in downtown Montreal in the past and I'Ve always appreciated them.

It is very good and very simple instructions. almost to much hand holding.

Good Course. We covered a lot of material in a short amount of time. This course had useful labs that built upon each other.

Great and very intuitive. Better than the traditional hit the wrong button/lose points.