Course 1: AI, LLM & Prompt Engineering

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Course 1: AI, LLM & Prompt Engineering

This course is part of Agentic AI From Fundamentals to Production Specialization

Instructor: Vidya Subramanian

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 hour to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 hour to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

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This course is part of the Agentic AI From Fundamentals to Production Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 3 modules in this course

Course 1: AI & LLM Core Concepts and Prompt Engineering

------------------------------------------------------- What you will learn ------------------- (a) Explain how LLMs work, from transformers to the inference pipeline (b) Classify AI systems by capability and by the five levels of autonomy (c) Choose a model by architecture, tier, capability, and pinned version (d) Control output with temperature, top-P, penalties, and reasoning effort (e) Write prompts that produce reliable, structured results Why take it ----------- (a) Builds the foundation every later course assumes (b) Explains why identical prompts still give different answers, and what to do about it What makes it unique -------------------- (a) Provider-agnostic labs: call Anthropic, OpenAI, and Google models side by side on the same task (b) No prior AI experience required; reading Python is enough

Build a clear mental model of how AI, generative AI, and large language models relate. You’ll learn the four core AI abilities (perception, reasoning, action, and learning), how AI is classified by capability and approach, and how LLMs differ by openness (proprietary vs. open weights) and focus (enterprise vs. reasoning models). You’ll also learn the five levels of AI autonomy, from chatbot to super-agent, and weigh LLM capabilities against limitations like hallucination, inconsistent outputs, and knowledge cutoffs. In the lab, you’ll send the same prompt to Gemini, OpenAI, and Anthropic models and compare quality, cost, and recency.

What's included

1 video1 reading

Learn why the output gap isn’t always the model; sometimes it’s the prompt. You’ll design prompts using six components (persona, context, instructions, few-shot examples, guardrails, and output format), and apply techniques such as chain-of-thought, self-consistency, and chain-of-verification. You’ll follow the LLM inference process step by step, then control it with API settings: temperature, top-P, top-K, max tokens, stop sequences, penalties, seed, and reasoning effort. You’ll also learn context window limits, context rot, inference costs, and why LLMs are non-deterministic.

What's included

1 video1 reading

Check your understanding of LLM types, autonomy levels, prompt engineering techniques, and inference settings before moving on to agentic AI in Course 2.

What's included

1 assignment

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Vidya Subramanian

Kairos Decisions

6 Courses415 learners

Offered by

Kairos Decisions

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