Build a foundation in how AI agents work by understanding the LLMs and prompting that power them as you build agentic systems.
Duration
3 weeksWeekly study
3 hours100% online
How it worksIncluded in an ExpertTrack
Course 1 of 4
Agentic AI: Prompt Engineering and LLM Essentials
Learn how Large Language Models and prompting power modern AI agents
Before building effective agents, you must first understand the engine that powers them.
In this three-week course, you’ll learn how Large Language Models (LLMs) work and how to design prompts that transform them from simple text generators into reliable, agentic systems capable of reasoning, planning, and tool use.
You’ll gain hands-on experience building working AI systems in Python, learning the core principles behind prompt engineering, structured outputs, and agent design patterns.
Understand prompt engineering and LLM reasoning
LLMs are powerful but unpredictable without proper design. On this course, you’ll explore how they reason, where they fail, and how issues such as hallucination, token limits, and context window constraints impact performance.
You’ll develop practical prompt engineering skills using techniques such as chain-of-thought prompting, self-ask prompting, and few-shot learning. These methods help guide models toward more accurate and structured reasoning, forming the basis of effective AI prompt engineering.
Learn context engineering and structured outputs
Next, you’ll learn context engineering principles and how to structure information so that LLMs can reason effectively and consistently.
Build AI agents using core design patterns
Finally, you’ll implement three core agentic design patterns in code: ReAct, Reflection, and Plan and Execute.
You’ll compare how each pattern performs on the same problems and learn when to apply each approach in practice. By combining these methods, you’ll build more capable AI agents that can plan, reason, and adapt.
Syllabus
Week 1
Build Your Prompt Engineering Foundation
Navigating Your Prompt Engineering Journey
Get oriented before you begin. This activity introduces the course structure, invites you to share your goals, and prompts you to reflect on your existing knowledge of prompt engineering.
Start with Generative AI and LLMs
This activity introduces learners to Generative AI, foundation models and LLMs. It also helps them understand why LLMs need prompts, context, tools, memory and workflows to become useful in real applications.
Learn the Basics of Prompt Engineering
This activity helps learners understand prompt engineering as the practice of designing, testing and refining instructions. It also reinforces prompt clarity, specificity, context, audience and output style.
Set Up and Write Agentic System Prompts
Learners move beyond basic prompts into context engineering, context management, persistent context pipelines and structured reasoning prompts.
Week 2
Engineer Context and Structure Better Reasoning
Move Beyond Prompt Engineering
This activity explains why prompts alone are not enough. You will explore how context engineering brings together instructions, conversation history, external knowledge, tools, memory, state, and structured outputs.
Manage Context in Agentic Systems
This activity examines how agents manage short-term context, token limits, conversation history, and tool results. You will also see how a Python application can store, retrieve, format, and pass context across separate runs.
Use Structured Reasoning Prompts
This activity compares direct prompts, short reasoning summaries, self-ask prompts, and ReAct-style prompting. The focus is on making multi-step outputs easier to verify, audit, and improve.
Week 3
Design and Build Agentic AI Workflows
Move into Agentic AI and Frameworks
This activity introduces agentic AI as goal-driven workflows, covering autonomy, context awareness, tool use, and frameworks such as LangChain, CrewAI, LangGraph, Langfuse, and LlamaIndex.
Explore Agentic Design Patterns
This activity covers ReAct, reflection and pattern selection. Learners see how agents reason with tools, review and improve outputs, validate quality, retry when needed and produce more reliable results.
Plan, Review and Execute Agent Workflows
Explore planning, validation, routing, and human approval in plan-and-execute workflows that convert user tasks into structured, traceable, and controlled execution.
Prove you're job ready
Highlight the new, job-relevant skills you’ve gained and supplement existing qualifications with a hard-earned, industry-specific digital certificate – plus one for every course within your ExpertTrack.
- Learn the latest in your chosen industry or subject.
- Complete each course and pass assessments.
- Receive certificates validated by the educating organisation.
- Impress employers with learning outcomes you can add to your CV.
- Make your career dreams a reality.
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Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- Explain how generative AI, foundation models, and large language models support language-based AI applications.
- Apply prompt engineering techniques, including zero-shot, one-shot, few-shot, self-ask, and structured prompting, to improve LLM responses.
- Develop python-based agent workflows using core design patterns such as ReAct, Reflection, and Plan-and-Execute.
- Evaluate agent outputs using validation checks, retry logic, structured planning, and human review before execution.
- Investigate how context engineering, context windows, token limits, conversation history, and tool observations affect agentic AI workflows.
Who is the course for?
This course is for technology enthusiasts, software developers, data professionals, product managers, and business leaders who want to understand agentic AI.
No prior AI engineering experience is required, though basic familiarity with concepts like APIs and Large Language Models will be helpful. It is ideal for professionals exploring career transitions into AI engineering, team leads evaluating agentic AI for business applications, and curious learners who want to move beyond chatbots and understand autonomous AI systems.
What software or tools do you need?
Learners need a computer (Windows, macOS, or Linux) with at least 8 GB RAM and a stable internet connection. Python 3.10 or later should be installed, along with a code editor such as VS Code. No paid software or specialised hardware is required.
Who will you learn with?
Who developed the course?
Edureka
Edureka is a global online learning platform offering industry-focused training in emerging technologies such as Cloud, AI, GenAI, Data Science, DevOps, and Cybersecurity, helping professionals upskill and advance their careers.
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