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AI and LLM fundamentals

Use this after studying the full explanations. Spend 10 minutes on vocabulary, 10 on RAG and agents, 10 on security/workflows/evaluation, and 10–30 on the insurance design and scenario answers.

A practical textbook for building AI applications, agents, and reliable client solutions.

Artificial intelligence, or AI, is the broad field of making software perform tasks such as recognizing speech, finding patterns, or understanding language. Machine learning, or ML, learns patterns from examples instead of requiring a rule for every case.…

A large language model, or LLM, is a model trained on large amounts of data to process and generate language. It learns patterns in how text is arranged and can use those patterns for summarization, extraction, explanation, and tool selection. It does not…

Tokens are the small units of input and output processed by a model. A token might be a word, part of a word, punctuation, or another encoded unit. Token counts depend on the tokenizer, language, and content; characters and words are only rough estimates.

A context window is the amount of model input and generated content a model can handle within its documented limits. Context is the particular information you provide for one call: instructions, messages, evidence, and tool results. A large window gives…

Temperature changes how sharply a model favors its higher-scoring next-token choices. Lower values generally reduce variation; higher values allow more variation. Top-p, also called nucleus sampling, restricts selection to a set of tokens covering a chosen…

Message roles identify the purpose and source of information. System or developer instructions describe application behavior, user messages contain requests, assistant messages contain model responses, and tool results supply external observations. Exact role…

Prompt engineering is writing clear task instructions, inputs, constraints, and output expectations. Zero-shot prompting gives instructions without examples; few-shot prompting adds a small number of input/output examples. A prompt is part of the application…

JSON output is machine-readable data, such as an object with named fields. Structured output adds a declared schema so the response follows expected fields and types when the provider supports schema-constrained generation. Valid JSON, valid schema, and valid…

Function calling lets a model propose a named function and structured arguments. Tool calling is the broader pattern that includes functions and capabilities such as search or code execution. A proposed tool call is an untrusted request that your application…

AI and LLM fundamentals

Use this after studying the full explanations. Spend 10 minutes on vocabulary, 10 on RAG and agents, 10 on security/workflows/evaluation, and 10–30 on the insurance design and scenario answers.

Practice saying the answer clearly, then explain the insurance example and the trade-off. A strong answer identifies the authoritative data, safe failure, and measurement—not only the framework name.

Beginner — What makes a good prompt?

Beginner — What is RAG?

Beginner — Explain an embedding.

Beginner — Why use a vector index?

Beginner — What does LangChain provide?

Beginner — What are nodes, edges, and state?

Beginner — Agent versus chatbot?

Beginner — What problem does MCP solve?

Beginner — What is MAF?

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module