Prompt Engineering Fundamentals — Prompt Engineering & Evaluation Roa…

Beyond typing better questions

Steps in Prompt Engineering Fundamentals

What LLM Application Development Involves — beginner · Building products on top of foundation models rather than training them
What Prompt Engineering Actually Is — beginner · Beyond 'typing better questions': a real engineering discipline
What LLMOps Actually Covers — beginner · Extending MLOps practices for the specifics of LLM-based systems
Why RAG Exists — beginner · Grounding model output in real data instead of relying on parametric memory
What Fine-Tuning Actually Changes — beginner · Adjusting model weights versus just changing the prompt
What Makes Something an AI Agent — beginner · Distinguishing agents from simple chatbots and single-turn features
The Modern LLM App Stack — beginner · APIs, orchestration, vector stores and frontends that make up a typical stack
Why Prompt Wording Changes Output So Much — beginner · How models interpret phrasing, structure and framing
LLMOps vs Traditional MLOps — beginner · What's genuinely new versus what's the same discipline in new clothes
A Brief History of Model Adaptation Techniques — beginner · From full fine-tuning to today's parameter-efficient methods
The Agent Loop: Observe, Think, Act — beginner · The basic cycle underlying most agent architectures
RAG vs Fine-Tuning — beginner · When retrieval solves a problem that fine-tuning doesn't, and vice versa
Choosing a Foundation Model Provider — beginner · Comparing major model providers on capability, cost and latency
Types of AI Agents — beginner · Reactive, deliberative and hybrid agent architectures compared
Prompt Engineering vs Fine-Tuning — beginner · When adjusting instructions is enough, and when it isn't
The LLMOps Lifecycle — beginner · From prompt/model development through production monitoring
The Basic RAG Pipeline — beginner · Ingest, embed, store, retrieve, generate as the core loop
Open-Weight vs Closed Models for Fine-Tuning — beginner · Which models can actually be fine-tuned, and how
The Fine-Tuning Workflow Overview — beginner · Data prep, training, evaluation and deployment as a full cycle
API Keys, Authentication and Rate Limits — beginner · The practical basics of calling a hosted LLM API
Common LLMOps Failure Modes — beginner · Where production AI systems typically break down
The Prompt Engineering Workflow — beginner · Draft, test, measure, iterate as a repeatable cycle
Where Agents Add Real Value — beginner · Recognizing tasks that genuinely benefit from autonomy versus simpler automation
Where RAG Fits in an AI Product — beginner · Common use cases: support bots, internal search, document Q&A
Realistic Expectations for Fine-Tuning Results — beginner · What fine-tuning can and can't fix about model behavior
Common Prompt Engineering Myths — beginner · Separating genuinely useful techniques from folklore
Your First LLM API Call — beginner · Making a basic request and understanding the response format
The Current State of Agent Reliability — beginner · Being realistic about what agents can and can't be trusted to do today
Roles and Responsibilities in LLMOps — beginner · How this work is typically split across platform, ML and product engineering
Limitations of Naive RAG — beginner · Why a basic implementation often disappoints in production
Setting Up a Prompt Development Environment — beginner · Tools for iterating on and comparing prompts efficiently
Setting Up Your First RAG Prototype — beginner · Getting a minimal retrieval-and-generate pipeline running end to end
Setting Up a Minimal LLMOps Practice — beginner · The smallest reasonable setup for a team just starting out
Setting Up a Development Environment for LLM Apps — beginner · SDKs, local testing and iteration loops
Setting Up Your First Fine-Tuning Experiment — beginner · Running a minimal fine-tuning job end to end
Building Your First Simple Agent — beginner · A minimal agent that can call one tool and return a result

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Prompt Engineering & Evaluation roadmap — the full learning path

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