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Production Prompt Engineering for LLM Systems
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.
Requirements
- Comfort using chat-completion APIs and message roles (system/user)
- Basic JSON literacy (objects, arrays, types)
- Familiarity with building software workflows (tools, requests, validation)
- Awareness of common LLM failure modes (hallucination, format drift)
What you'll learn
- Design reusable prompts with clear role hierarchy, constraints, and delimiter-based isolation.
- Choose between few-shot examples and chain-of-thought directives based on task type, reliability, and cost.
- Specify structured outputs with schemas, and enforce formats using stronger mechanisms than prompt wording alone when needed.
Learning path
7 modules • Each builds on the previous one
1
Prompt message architecture and hierarchy
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.
2 videos10 min
2
Few-shot prompting pattern design
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.
1 video7 min
3
Structured outputs, schemas, and validation
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.
2 videos10 min
4
Tool calling and agent prompt loops
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.
2 videos9 min
5
Long-context prompting and RAG patterns
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'.
2 videos9 min
6
Prompt security: injection and isolation
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.
2 videos8 min
7
Prompt evaluation, regression, and token economics
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.
2 videos10 min
Start Learning
Begin your learning journey
Modules7
Duration60 min
Science-backed learning
In-video quizzes and scaffolded content to maximize retention.
Key concepts
Instruction Hierarchy And Prompt StructureConstraints, Delimiters, And Output-Format ContractsFew-Shot Demonstrations Versus Reasoning Scaffolds