Prompt Engineering and LLM App Fundamentals

About this course

Most prompt-engineering content is fluff. This isn't. We treat LLMs like the noisy, statistical tools they are, and design real systems on top — chunking strategies, retrieval-augmented generation, structured outputs, tool calling, evaluation, and the cost-vs-quality decisions every team eventually faces. By the end you've shipped three working tools (a documentation Q&A bot, a structured-data extractor, a workflow agent) using both Gemini and Claude APIs, and you can compare them honestly on cost, latency, and quality.

What you'll cover

• 1

  How LLMs actually work (just enough to design well)

  Tokens, attention, temperature, and why context length matters. No PhD required.

• 2

  Prompt design patterns that work

  Few-shot, role prompts, structured templates. The patterns that beat 'just be clear and specific'.

• 3

  Structured outputs and JSON schemas

  Force the model to return parseable JSON. The single biggest reliability win.

• 4

  Retrieval-augmented generation (RAG)

  Embeddings, chunking, vector search. Build a Q&A bot over your own documents.

• 5

  Tool calling and agents that don't lie

  Give the model real tools (search, code execution, your APIs). Constrain it from hallucinating.

• 6

  Evaluation: how do you know it's good?

  Build evals before scaling. Golden sets, LLM-as-judge, regression catching.

• 7

  Cost, latency, and choosing your model

  Gemini Flash vs Claude Sonnet vs GPT-4. The tradeoffs that matter at production scale.

Skills you'll build

• Gemini API
• Claude API
• RAG
• embeddings
• tool calling
• structured outputs
• evaluation
• AI engineering