What is the 'Building LLM Applications' training about?

This training delves into Large Language Models (LLMs) and state-of-the-art generative AI, focusing on core concepts, diverse prompting techniques, and strategies to mitigate LLM risks and limitations. You will gain hands-on experience in crafting robust and reliable LLM applications.

Who is this LLM application development training designed for?

This training is ideal for experienced Python developers who have experimented with commercial LLM services (e.g., Gemini, ChatGPT, Dall-E) and are looking to create their own LLM applications in Python. It is also suitable for teams and organizations aiming to upskill their employees.

What specific skills will I learn during this LLM training?

You will learn to explain core LLM & generative AI concepts, enable models to answer questions from untraind documents, create your own LLM applications, conduct searches on vectorized databases, enhance LLMs with external information using Retrieval Augmented Generation (RAG), and create LLM Agents that can perform actions and self-determine information needs.

What is the duration and format of the LLM Application Development course?

The course is 4 days long. When delivered virtually, it is split into half-days (4h) instead of full days (8h). Both in-person (Amsterdam) and virtual options are available depending on the scheduled dates.

How much does the 'Building LLM Applications' training cost?

The price varies by date and format. For example, a 2-day in-person session in Amsterdam is €1425,- (excl. VAT), and a 2-day virtual session is €1315,- (excl. VAT). A Summer School special offers a 30% discount on select dates.

Are there any prerequisites or prior knowledge required for this course?

Yes, you should be an experienced Python developer and have played around with commercial LLM services like Gemini or ChatGPT. This training focuses on building applications, not on the foundations of Deep Learning or deploying models in production (for which separate courses like LLMOps or MLOps are recommended).

Is a certification provided upon completion of the training?

No, this training does not include a certification.

ACE AI-Native Software Engineering^TM

Reimagine Software Development

Xebia's ACE AI-Native software engineering^TM framework is designed for pragmatic, outcome-driven modernization and digital transformation. It uses AI to amplify engineering expertise, delivering measurable impact through structured, scalable workflows that improve efficiency and drive real business results.

Shifting Software Development Paradigms

Transformative Potential

AI has the potential to solve one of software engineering’s biggest challenges: delivering more value faster despite the ever-growing complexity that slows teams down.

Barriers to Adoption

Adoption remains limited - AI is mostly used for isolated tasks. Skills gaps, weak engineering foundations, and security concerns are holding back real change and fueling excessive caution.

Elusive ROI

To unlock true value, AI must go beyond code. The key to meaningful transformation is applying it across the entire SDLC, improving engineering practices, and tracking measurable impact.

Business Value in Action

Accelerate, Modernize & Scale with AI

Hi-Velocity Product & Platform Delivery

Boost delivery speed by up to 40%, go to market faster, reduce costs, and stay focused on results.

Up to 40%

Next-Gen Legacy System Modernization

Accelerate legacy transformation with 50–70% lower cost and time, minimal disruption, and seamless navigation through complex, poorly documented systems.

Up to 70%

Software Engineering Transformation

Achieve enterprise-scale efficiency with 30–50% faster delivery, reduced risk, and higher-quality software—enabling consistent, cost-effective performance across the organization.

Up to 50%

From idea to impact

Solution Overview

Our solution streamlines every phase of the SDLC with composable AI, measurable outcomes, and empowered engineering teams.

Discover Key Features

Whether you're building from scratch or modernizing legacy systems, Xebia meets you where you are. We turn raw ideas into deployable assets and bring clarity to legacy code, offering safe, incremental modernization without full rewrites.

Requirements Builder

Transforms raw, unstructured business inputs into structured, testable software requirements using contextual AI.

- Parses notes, diagrams, screenshots, and more
- Defines business context to focus AI output
- Refines output via iterative prompting
- Aligns analysis with ISO and compliance standards
- Automatically detects ambiguities, gaps, and inconsistencies
- Generates outputs with Gherkin, criteria, edge cases & value
- Ranks user stories using MoSCoW and RICE
- Recommends team setup and delivery timeline
- Cuts time-to-spec from days to minutes

Test Case Generator

Automatically converts structured software requirements into executable, human-readable test scenarios.

- Parses structured requirements to uncover feature logic
- Auto-generates test cases in formats like Gherkin
- Reduces QA workload by eliminating manual test design
- Expands test coverage by detecting hidden edge cases
- Aligns tests with business logic for full consistency
- Speeds up time-to-test by converting specs in minutes

Architecture Generator

Transforms structured requirements into system design diagrams and technical artifacts — automatically and consistently.

- Uses structured specs with value, logic, and business intent
- Auto-generates full C4 diagrams: context to code level
- Builds API specs from system logic and functional inputs
- Designs entity models with states, relations, lifecycles
- Creates deployment views aligned with infra requirements
- Produces HLD and LLD docs ready for engineering use
- Maps system flows across APIs, databases, and services
- Aligns tech teams around one shared architectural vision
- Speeds up design and boosts clarity with visual assets
- Converts specs into machine-readable design instructions

UX Design Generator

From Structured Requirements to Clickable Prototype — AI-Driven Design Acceleration.

- Converts specs into PRDs with flows, features, and edge cases
- Generates style guides with colors, fonts, and UI patterns
- Builds screen lists from user journeys and use case logic
- Auto-generates wireframes with layout and CTA structure
- Creates UX specs with nav logic, error states, and rules
- Integrates with tools to instantly render clickable prototypes
- Aligns design and dev with traceable UI/UX-to-spec flow

Test Code Generator

Turns natural-language test scenarios into automation-ready test code using your preferred tech stack.

- Converts test cases into executable code instantly
- Supports Cypress, Playwright, Selenium, and JUnit
- Matches your team’s coding style and tech stack
- Eliminates manual scripting for faster automation
- Enables plug-and-play testing in CI/CD pipelines
- Connects business intent with real-world execution

Project Explorer

Maps and documents the structure, tech debt, and modernization potential of legacy codebases using explainable AI.

- Maps your codebase with AI-powered file summaries
- Spots complex areas and maintainability challenges
- Suggests modernization based on your current tech stack
- Builds a reusable system understanding for planning
- Flags high-impact refactor or replatform candidates
- Speeds up onboarding for unfamiliar codebases

Modernization Planner

Designs, compares, and tailors upgrade strategies for legacy systems based on code insights and business logic.

- Recommends upgrade paths from legacy to modern stacks
- Offers tailored “recipes” based on best architecture practices
- Compares upgrade scenarios to assess trade-offs and cost
- Extracts logic and intent from legacy code without docs
- Ranks upgrade priorities by feasibility and business impact
- Enables faster, safer transformation with data-driven plans

Our Ecosystem

Partners

View All

Powering the Future of Software Engineering with AI. Built on AWS. Powered by Amazon Bedrock. AWS Premier Partner with 750+ experts and 18 competencies, leading AI-powered modernization with GenAI, Agentic AI, and digital resilience.

Global GitHub Partner of the Year 2024-2025 and GitHub's first verified partner in Europe.

Xebia is partnering with Harness to redefine software delivery, integrating true agile platform engineering with AI-driven automation to empower development teams and accelerate business transformation.

IBM Watson offers AI-powered solutions that help businesses automate processes, gain insights from data, and make smarter decisions. From natural language understanding to predictive analytics, Watson enables enterprise-grade AI at scale.

Rhino.ai enables enterprises to unlock business value through scalable machine learning solutions, intelligent automation, and advanced analytics across the enterprise lifecycle.

Our Ideas

Insights & Perspectives

View All

‌

FAQs on Xebia ACE AI-Native Software Engineering

Accelerate your SDLC with Xebia ACE AI-Native Engineering

With Xebia ACE, our AI-Native Software Engineering Solution, you embed intelligence across the entire SDLC - from requirements to deployment - unlocking measurable productivity gains, faster delivery, and higher quality at every stage.

Fill in the form below to request a guided walkthrough of our AI-Native Software Engineering Solution.

Frequently Asked Questions

Unlike point solutions that target single tasks, ACE is an end‑to‑end framework that integrates AI into every SDLC phase (requirements, architecture, code, tests, deployment). It focuses on pragmatic, outcome‑driven modernization with explainable automation (e.g., Project Explorer, Modernization Planner) and is built on enterprise partnerships (AWS, Amazon Bedrock, GitHub, Harness) for production readiness and scale.

Contact

Let’s discuss how we can support your journey.

‌

Response

Related Topics

Context Files

Related Topics

ACE AI-Native Software Engineering^TM