LLMs & Prompt Engineering

Description

Getting reliable output from language models — prompt structure, context, retrieval, evaluation, and the failure modes to design around.

A working course on using language models as components in real systems. It covers how these models behave, prompt structure that produces consistent results, giving models the right context including retrieval, evaluating output at scale, and designing around hallucination and drift rather than hoping they don’t happen.

Who this is for

• Developers building features on top of language models
• Technical writers, analysts and operators who use these tools daily and want reliability
• Product people specifying AI features
• Anyone getting great results in testing and inconsistent results in production

What you’ll be able to do

• Explain how these models generate output, enough to predict their failure modes
• Structure prompts that produce consistent, parseable results
• Supply context effectively, including when to use retrieval
• Build an evaluation set and measure prompt changes instead of guessing
• Design around hallucination with grounding and verification
• Manage cost and latency in a production system

Syllabus — 5 modules, 31 study hours

1. How these models behave (5 hrs)
2. Prompt structure (7 hrs)
3. Context and retrieval (7 hrs)
4. Evaluation (6 hrs)
5. Production concerns (6 hrs)

Includes a 5-week study plan, practice tasks with stated learning outcomes, an honest readiness self-assessment and a progress tracker.

Independent study material. Completing it does not award a certification or credential.