Generative AI in Development: Building Tools, APIs, and Intelligent Agents

Generative AI in Development: Building Tools, APIs, and Intelligent Agents

This Course at a Glance

• Learn how to build practical AI applications
• Explore how APIs, frameworks and intelligent agents work
• Develop your own AI prototypes
• Gain technical skills relevant to careers in business, finance and tech
• Study our artificial intelligence courses online, in your own time

If you’re interested in how to go beyond using AI tools and start building with them, this course is the ideal next step.

Whether you're in a technical role or exploring the potential of AI in your organisation, this course gives you practical, project-based experience with today’s most important GenAI technologies. You’ll work with APIs, build intelligent agents, experiment with vector databases, and explore frameworks like LangChain and PromptFlow.

This is one of our most in-depth AI tools courses, ideal for learners who want to turn ideas into working AI prototypes.

From prompting via Python to deploying apps with Streamlit or Flask, this course connects theory with real-world build skills and helps you bring your concepts to life using accessible, industry-relevant tools.

If you're looking for AI design courses or practical artificial intelligence courses that prepare you to create value with GenAI, this programme is a perfect fit.

Getting started

At learndirect, flexibility comes first. You can start this course at any time and complete it at your own pace with no fixed lessons or schedules. It’s built for working professionals, developers-in-training, product managers, and career changers looking to gain applied AI skills through interactive learning. Everything is delivered online, so you can learn when and where it suits you.

Modules

Unit 1: Introduction to Generative AI

Upon successful completion of this module, you will:

• Understand what Generative AI is.

• Trace the history and evolution of Generative AI, including key concepts like GANs, VAEs, and Transformers.

• Differentiate between Generative and Discriminative AI models.

• Identify real-world applications and industries currently leveraging Generative AI.

Unit 2: Generative AI Architecture and Ecosystem Overview

Upon successful completion of this module, you will:

• Review foundational models such as Large Language Models (LLMs), Diffusion models, and Multimodal AI.

• Explore the landscape of key Generative AI tools, including OpenAI, Hugging Face, LangChain, and Replicate.

• Understand the difference between using APIs and low-code platforms for AI integration.

• Learn how Generative AI tools "plug into" and enhance real-world applications.

Unit 3: Working with the OpenAI API

Upon successful completion of this module, you will:

• Understand API authentication and various OpenAI endpoints (Chat, Completion, Image).

• Learn how to prompt programmatically (e.g., using Python with GPT-4).

• Master key API parameters such as temperature, max tokens, and top_p for controlling AI outputs.

• Implement effective error handling and manage rate limits when working with APIs.

Unit 4: Prompt Engineering for Developers

Upon successful completion of this module, you will:

• Build dynamic prompts using templates for flexible AI interactions.

• Implement context injection, prompt chaining, and history management for complex conversations.

• Apply best practices for ensuring reliability and precise output control from LLMs.

Unit 5: Building with LangChain and Other Frameworks

Upon successful completion of this module, you will:

• Gain an overview of popular AI development frameworks like LangChain and PromptFlow.

• Understand core concepts within these frameworks: chains, tools, memory, and agents.

• Learn to build a functional question-answering application integrated with a document source.

Unit 6: Vector Databases and Retrieval-Augmented Generation (RAG)

Upon successful completion of this module, you will:

• Be introduced to embeddings and their role in similarity search.

• Explore essential vector database tools such as Pinecone, Weaviate, and Chroma.

• Learn how to integrate a vector database with LLM prompts for Retrieval-Augmented Generation (RAG).

Unit 7: Building Custom AI Agents

Upon successful completion of this module, you will:

• Understand the definitions and design principles of AI agents versus simple chatbots.

• Explore agent capabilities including tool use, planning, and reasoning.

• Learn about multi-agent collaboration patterns, referencing concepts like Auto-GPT and CrewAI.

Unit 8: Deploying GenAI Apps

Upon successful completion of this module, you will:

• Discover various hosting options for Generative AI applications, such as Streamlit, Gradio, and Flask.

• Understand security basics for protecting API keys and user data.

• Learn methods for sharing prototypes on the web or within Learning Management Systems (LMS).

Unit 9: Ethics, Limitations, and Scaling Considerations

Upon successful completion of this module, you will:

• Address critical issues such as API costs, data privacy, and responsible AI building.

• Identify performance bottlenecks and learn optimisation techniques for Generative AI applications.

• Understand key considerations for scaling and production-readiness of AI solutions.

Entry Requirements

You don’t need to be a professional developer, but some basic Python knowledge will be useful. This course is ideal for learners with a technical mindset, from business analysts to junior developers and innovators, who want to start building real-world GenAI tools and solutions.

Average completion timeframe

You’ll have complete flexibility to learn at your own pace. Most learners complete the course within a matter of weeks, studying for a few hours each week.

Assessment requirements

After each lesson there will be a question paper, which needs to be completed and submitted for marking. This method of continual assessment ensures that we can consistently monitor your progress and can provide you with assistance throughout the duration of the course. You will receive a digital certificate (e-cert) issued by TQUK upon successful completion of the course.

Course fees

All course fees, inclusive of all payment plans including our Premium Credit Limited option, must be settled before certification can be ordered.

*You will have access to the course for 24 months.

TQUK

This course is officially endorsed by Training Qualifications UK (TQUK), a nationally recognised awarding organisation regulated by Ofqual. TQUK endorsement confirms that the course content, delivery and assessment have been rigorously reviewed for quality, consistency and relevance to industry standards. Learners can be confident that they are engaging with a professionally recognised programme that supports career development and lifelong learning.

Established in 2013, Training Qualifications UK are one of the most forward-thinking and agile Awarding Organisations in the UK. They work closely with both employers and providers to ensure learners receive qualifications that have impact in the workplace and help learners succeed in all walks of life.

This course prepares you for fast-changing roles where AI development and integration skills are in high demand. It’s ideal for anyone looking to expand their capabilities in:

• Data or business analysis
• Technical marketing or product design
• AI-enabled development or integration
• Innovation and R&D roles
• Creative tech and digital strategy

Whether you're in tech, finance, product, or operations, this course is a launchpad to build with AI, not just use it.

Frequently Asked Questions