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Created by James Quigley
Design robust, testable prompts for software-integrated LLM workflows. You will learn how to structure message roles, enforce machine-reliable outputs, drive tool calls safely, handle long context with RAG, defend against prompt injection, and iterate with regression tests and token-cost controls.
7 modules • Each builds on the previous one
Design robust prompts using role separation (system/developer/user), instruction precedence, and reusable templates. Emphasize how to isolate data from instructions with delimiters and how to structure multi-part tasks without ambiguity.
Use demonstrations (input→output pairs) to teach the model a target format and decision boundary. Clarify the difference between few-shot (pattern induction) and chain-of-thought directives (reasoning scaffolds), and how to choose, order, and compress exemplars.
Make outputs machine-reliable using explicit JSON shapes, JSON Schema constraints, and validation/retry strategies. Cover common failure modes (hallucinated keys, wrong types, extra text) and prompt patterns that reduce them.
Design prompts for tool use (function calling), including tool selection, argument construction, and handling tool errors. Introduce agent loop patterns (planner-executor, ReAct-style) and when to prefer simpler pipelines.
Engineer prompts for long documents using chunking, map-reduce summarization, retrieval-augmented generation (RAG), and positional-bias mitigations. Include token budgeting, citation patterns, and strategies to reduce 'lost in the middle'.
Harden prompts against direct/indirect prompt injection, tool injection, and data exfiltration attempts. Cover isolation techniques (separating untrusted content, allowlists, tool permissions), and safe refusal and logging patterns.
Create an optimization loop: define success metrics, build a representative eval set, run regression tests, and control cost/latency. Emphasize why verbose reasoning increases billed tokens and how to reduce errors without exploding output length.
Begin your learning journey
In-video quizzes and scaffolded content to maximize retention.