Core Sections
From foundational concepts through modern generative AI workflows.
INTERACTIVE TECHNICAL DOCUMENTATION
AI Development Field Guide
From Fundamentals to Generative AI
A structured introduction to how modern AI systems are understood, designed, evaluated, and improved — built as an interactive learning experience for developers, technologists, and curious problem-solvers.
- AI Fundamentals
- Machine Learning
- Generative AI
- RAG
- Evaluation
- Responsible AI
ai_workflow.py
define
problem
collect
data
build
model
evaluate
results
improve
system
Human-led workflow
Guided Path
A recommended sequence for moving from concepts to practical application.
Iteration
AI development depends on repeated testing, evaluation, and improvement.
02
GUIDED LEARNING
Follow the development path.
AI can feel enormous when every concept is presented at once. This guide organizes the subject into a practical sequence.
FOUNDATION
Understand the problem
Define the outcome before selecting algorithms, models, or tools.
Explore fundamentals →DATA
Understand the information
Identify what data exists, what it represents, and what limitations it carries.
View the workflow →MODELING
Learn from patterns
Use machine-learning concepts to understand how systems can identify useful patterns.
Explore machine learning →GENERATION
Work with generative systems
Understand how modern systems produce text, images, code, and other outputs.
Explore generative AI →QUALITY
Evaluate the result
Measure whether the system solves the intended problem reliably and usefully.
Explore evaluation →RESPONSIBILITY
Consider the impact
Examine privacy, bias, transparency, security, oversight, and human consequences.
Explore responsible AI →03
FOUNDATIONS
Start with the vocabulary.
These terms are related, but they do not mean the same thing. Understanding the hierarchy makes the rest of AI development easier to reason about.
Artificial intelligence is the broad field concerned with creating computational systems that perform tasks associated with human-like reasoning, perception, learning, language, or decision-making.
Think of it as:
The broadest umbrella.
Machine learning is an approach in which systems improve task performance by finding useful patterns in data rather than relying only on manually written rules.
Think of it as:
Learning useful patterns from examples.
Deep learning uses multi-layered neural networks to learn increasingly complex representations from data.
Think of it as:
A powerful family of machine-learning
techniques.
Generative AI refers to systems designed to produce new outputs such as text, images, audio, code, or structured content based on learned patterns.
Think of it as:
Models that generate new content.
Artificial Intelligence
Machine Learning
Deep Learning
Generative AI
04
DEVELOPMENT WORKFLOW
Good AI starts before the model.
The quality of an AI system depends on more than choosing an algorithm. Strong development begins with the problem and continues through evaluation and monitoring.
Define
Clarify the problem, expected outcome, constraints, users, and success criteria.
Gather
Identify useful data and understand its origin, quality, limitations, and permissions.
Prepare
Clean, transform, structure, and inspect data before modeling.
Build
Select an appropriate approach and create the initial model or system.
Evaluate
Measure performance against meaningful criteria rather than relying on impressions.
Improve
Diagnose weaknesses, adjust the system, retest, and continue iterating.
Monitor
Observe real-world performance, failures, changing data, and unintended outcomes.
CONCEPTUAL PYTHON
A simple preprocessing mindset
def prepare_records(records):
cleaned = []
for record in records:
if record is None:
continue
value = str(record).strip().lower()
if value:
cleaned.append(value)
return cleaned
Development principle
A sophisticated model cannot automatically repair a poorly defined problem or unreliable data pipeline.
05
MACHINE LEARNING
Learn from patterns in data.
Machine-learning approaches differ according to the type of information available and the task the system must perform.
LABELED EXAMPLES
Supervised Learning
The system learns relationships between examples and known target outcomes.
- Classification
- Regression
- Prediction
UNLABELED PATTERNS
Unsupervised Learning
The system explores structure or relationships without predefined target labels.
- Clustering
- Pattern discovery
- Dimensionality reduction
FEEDBACK
Reinforcement Learning
An agent learns behavior through interactions and feedback associated with its actions.
- Actions
- Rewards
- Policies
MODEL DEVELOPMENT
Training is only part of the process.
Separating data into distinct roles helps developers measure whether a model can perform well beyond the examples it learned from.
Training
Learn
Validation
Tune
Testing
Verify
06
MODERN AI
Systems that generate.
Generative systems create new outputs by modeling patterns learned from large collections of examples.
01
Input
Prompt, image, audio, structured data, or other context.
02
Model
Processes context and predicts useful output patterns.
03
Output
Text, code, image, audio, or another generated artifact.
STRENGTHS
Useful for exploration.
- Drafting and ideation
- Information synthesis
- Code assistance
- Structured transformation
- Natural-language interaction
LIMITATIONS
Output still requires judgment.
- Generated information may be inaccurate
- Context can be misunderstood
- Bias may be reflected in results
- Sensitive information requires care
- Important decisions need verification
07
PROMPT ENGINEERING
Better context creates better direction.
A useful prompt gives the system enough information to understand the task, constraints, audience, and expected output.
What should the system do?
What should it know?
What boundaries matter?
What should the result look like?
WEAK
Explain cybersecurity.
STRONGER
Explain phishing to high-school students using plain language, one realistic example, three warning signs, and a short prevention checklist.
PROMPT TEMPLATE
Reusable structure
Task:
[Describe the goal]
Context:
[Provide relevant background]
Constraints:
[List boundaries, requirements, or exclusions]
Audience:
[Describe who will use the result]
Output:
[Specify format, tone, length, or structure]08
RETRIEVAL-AUGMENTED GENERATION
Give the model relevant information first.
Retrieval-Augmented Generation, commonly called RAG, combines information retrieval with generation so a system can use relevant external material when constructing an answer.
Why RAG matters
Retrieval can help a generative system work with information that is more specific to the user's documents, organization, or task.
09
MODEL EVALUATION
Measure what actually matters.
Evaluation asks whether the system produces useful results for the problem it was designed to solve.
Correctness
Is the output factually or functionally correct for the task?
Relevance
Does the output address the actual request rather than something adjacent?
Reliability
Does performance remain useful across different examples and conditions?
Human Judgment
Does the result satisfy the needs of the people who will actually use it?
Build
Test
Measure
Diagnose
Improve
10
RESPONSIBLE AI
Capability is not the only question.
Responsible development also considers whether a system should be used, who may be affected, and what safeguards are appropriate.
Privacy
Protect sensitive information and understand what data is being collected, stored, or shared.
Fairness
Examine whether system performance differs unfairly across people, populations, or contexts.
Transparency
Make important limitations and system behavior understandable where appropriate.
Security
Consider misuse, unauthorized access, manipulation, and other technical risks.
Human Oversight
Keep people appropriately involved when consequences are significant or judgment is required.
Accountability
Define who is responsible for system design, deployment, monitoring, and correction.
Responsible AI asks not only “Can we build it?” but also “How should it be used?”
11
QUICK REFERENCE
AI terminology, simplified.
Search or browse key terms used throughout the field guide.
- Algorithm
- A defined procedure or set of computational steps used to solve a problem.
- Dataset
- A structured collection of examples or information used for analysis or model development.
- Embedding
- A numerical representation designed to capture meaningful relationships between items.
- Generative AI
- AI systems designed to produce new content such as text, images, audio, or code.
- Inference
- The process of using a trained model to produce a prediction or output.
- Machine Learning
- A family of methods that learns useful patterns from data.
- Model
- A computational representation learned or configured to perform a specific task.
- Neural Network
- A computational architecture composed of connected processing units organized into layers.
- Prompt
- Input or instructions provided to a generative AI system.
- RAG
- Retrieval-Augmented Generation combines retrieved information with generative model output.
- Token
- A unit of text representation processed by many language-model systems.
- Training
- The process in which a model adjusts its internal parameters using examples or feedback.
PROJECT PURPOSE
Technical documentation designed to teach.
AI Development Field Guide combines front-end development, technical writing, information architecture, educational design, and AI concepts in one interactive web experience.
HTML5
CSS3
JavaScript
Technical Writing
AI / ML Concepts
Accessibility
Responsive Design
Explore the Source