Module 6 of 6Lesson 2 of 4~11 min
Choose how to build it
A no-code tool, direct integration of a model or a turnkey vendor solution: each option commits your product on control, cost and dependence. This lesson helps you compare these build paths with explicit criteria, then justify the choice that fits your context.
Lesson objective
By the end of this lesson, you will be able to compare build options (no-code platform, a model provider's API plus SDK, a vendor's built-in solution) on control, cost, latency, data and vendor dependence, then justify a choice.
Topics covered
- AI build or buy
- no-code AI
- LLM API
- vendor selection
- AI assistant
Where it fits
Evaluate, ship, monitor
How do you know the assistant is ready, launch it safely and improve it?
Lessons in this module
- Design the evaluation plan
- Choose how to build it (this lesson)
- Launch and run in production
- Assemble the design dossier
What you will learn in the course
This lesson is part of the course Build an AI assistant for your product
- Identify a use case that justifies an AI assistant and write its framing brief (problem, users, allowed actions, out of scope, success criteria).
- Design the conversational experience: entry point, tone, handling uncertainty, handoff to a human and response format.
- Choose and justify a knowledge strategy (instructions, injected context, RAG, fine-tuning) and a conversation memory strategy.
- Specify the assistant's tools and actions (data read, actions written, confirmation, permissions) and choose how to build it.
- Identify the risks (injection, leaks, excessive actions, costs) and design layered guardrails that go beyond the prompt.
- Design an evaluation plan with a reference dataset, criteria, grading methods, release thresholds and a regression rule.
- Define production metrics (product, quality, cost, latency), alert thresholds and the loop from user feedback to the evaluation set.
Related courses
- Design a RAG architecture that fits your productAdvanced · ~3 hr 30 min
- Evaluate an AI feature: test sets, metrics and LLM judgesAdvanced · ~3 hr
- Ship and monitor an AI feature in productionExpert · ~3 hr