Full Stack Development
Build for the web, from the interface people see to the APIs and databases behind it.
Explore courseAgentic AI & Generative AI
Connect the pieces of a useful AI workflow: a language model, relevant information, tools, and a way to assess the response.
- Duration
- 3 months
- Learning format
- Online
Is this your
next step?
Go beyond a single prompt. Explore language models, retrieval, and agents that work with tools.
Who this can suit
- Learners who want to understand how an AI application uses instructions, retrieved information and tools together.
- Developers exploring language-model APIs and the structure of retrieval and agent workflows.
- Students comparing building an AI application with simply using a chat interface.
Before you begin
Basic programming experience and familiarity with calling an API are useful preparation. Ask the team which programming background and tools the current course expects.
Skills to work towards
- Write prompts that distinguish instructions, context and the response you need.
- Explain the role of model APIs and LangChain in an AI application workflow.
- Describe how retrieval and vector databases supply relevant context to a model.
- Plan a tool-enabled workflow with evaluation and human oversight in mind.
What you’ll
learn.
Explore the core areas covered in Agentic AI & Generative AI. Ask our team for the current teaching plan and depth of each topic.
LLMs & prompt engineering
Explore how language models respond to instructions, context and structured prompts.
OpenAI API & LangChain
Work with model APIs and the building blocks used to connect AI application workflows.
RAG & vector databases
Retrieve relevant information and use it to give a model more useful context.
Agents, tools & deployment
Explore tool-enabled workflows, application deployment and the need for evaluation and human oversight.
Make the
ideas tangible.
An illustrative practice idea. The projects offered in a batch may differ.
A document question assistant
Use a small set of study documents to explore retrieval-augmented generation and check whether answers are supported by the source text.
- Choose a small set of documents you have permission to use and prepare questions whose answers can be checked in the text.
- Retrieve relevant passages from a vector database and pass that context with the question to a language-model API.
- Compare responses with the source passages, test questions the documents cannot answer, and record where human review is needed.
How is this different from a prompt-only course?
Prompt engineering is one part of the curriculum. The course also lists model APIs, LangChain, retrieval-augmented generation, vector databases, agents, tools and deployment, so it considers how the pieces of an AI application connect.
Do I need previous programming experience?
Some programming and API familiarity is useful for understanding the application work. Tell APK what you have built or studied and ask whether the course starts at a suitable level.
What format and duration are listed?
Agentic AI & Generative AI is listed as a three-month online program. Ask APK about the current timetable, fees, model or tool access, and what support is available during practical work.
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