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

AI and LLM fundamentals

RAG stands for retrieval-augmented generation. It retrieves relevant external information and provides that information to a model before asking for an answer. It connects language generation to a changing knowledge source without retraining the model.

LangGraph represents execution as a graph of steps sharing explicit state. Nodes do work and edges select the next step; the graph can contain both ordinary code and model decisions. It supports applications that need branching, repeated tool use, and…

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?

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

AI and LLM fundamentals

A decision framework is a repeatable sequence of questions that turns a requirement into justified architecture. Each added capability should solve a concrete gap. It prevents treating every client request as an opportunity to deploy every AI framework.

Ask what the user actually needs and what the existing system already knows. Exact lookups, arithmetic, validation, and known transitions usually belong in normal code. AI is useful when language/media variation or learned pattern recognition provides value…

Use an LLM for language transformation, interpretation, synthesis, and bounded proposals. A dedicated classifier, OCR engine, search index, or template may solve the narrower task better. Define acceptable uncertainty and what happens when the model cannot…

Ask whether the answer depends on external knowledge and whether the corpus is too large or variable to supply directly. Use RAG for private/changing knowledge with source references; use exact tools for live records. If a handful of permitted documents are…

Use narrow tools for fresh data and actions. MCP is useful when approved hosts need a shared discoverable integration, not merely because the model has one function call. Keep the existing business API behind either a direct wrapper or the MCP facade.

If next steps are fixed, use a sequence of functions or a workflow. If next information actions genuinely vary, a bounded agent may help. Use LangGraph when explicit state, branching, review pauses, or recovery justify it; a short read-only loop can remain…

Yes when steps depend on durable business state, external events, review, timers, compensation, or repeated retries. Distinguish conversational agent state from the canonical business process. Choose a queue for independent jobs and a durable workflow…

Determine impact, ambiguity, consent, reversibility, and organizational authority. Approval must show the concrete action and bind to its version/hash, actor, scope, and expiry. Recheck current rules at execution; a long wait can invalidate earlier…

Map every trust boundary: UI, uploads, parser, retrieval, memory, provider, tools, business APIs, caches, and traces. Minimize information and authority, enforce tenant/object/action checks, constrain egress, validate schemas and rules, and test adversarial…

Define success, severe failures, and risk-based datasets before optimizing prompts. Separate retrieval from answer checks and agent decisions from actual business outcomes. Include missing information, stale evidence, injections, denied access, retries, and…

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module