Use this roadmap as a flexible sequence, not a calendar. Build from watsonx orientation into prompt engineering, model building, retrieval, tuning, deployment, governance, and mixed review.
Start by understanding where watsonx.ai fits in AI engineering work. Identify the purpose of Prompt Lab, AutoAI, deployment spaces, vector indexes, tuning workflows, and prompt or model evaluation. This makes later product details easier to place in the lifecycle.
Study prompt templates, variables, foundation-model selection, generation parameters, and evaluation at the same time. A prompt is not ready just because it produces a fluent answer; review whether the output is accurate, grounded, repeatable enough, and suitable for the intended use.
Next, review how AutoAI builds experiments and compares generated pipelines. Focus on the responsibilities that remain with the AI engineer: selecting data, interpreting metrics, checking data quality, reviewing candidate pipelines, and deciding whether the result is ready for deployment.
Retrieval and tuning solve different problems. Retrieval brings relevant external context into a response, while tuning adapts model behavior from examples. Review vector indexes, data formats for tuning, and the decision points that determine whether prompt editing, retrieval, or tuning is the better next step.
Review deployment spaces, deployable AI assets, prompt-template deployment, and management after release. Then connect those topics to governance: approval, evidence, responsible AI, output evaluation, asset visibility, and ongoing review.
Use practice questions after each topic, then switch to mixed review once the individual workflows feel familiar. Missed questions should become targeted review tasks: generation settings, Prompt Lab purpose, AutoAI interpretation, vector-index use, deployment spaces, or responsible AI controls.
Use these DotCreds paths when you are ready to practice, compare options, or keep studying.
IBM AI Engineering is the credential this DotCreds guide is organized around. Use this page to understand the topic, then move into practice or the guided course when you are ready.
Start with the beginner guide and study roadmap, then use practice questions to find weak areas before you spend time rereading everything.
It can be worth studying when the skills match your target role, current experience, and next job move. The related certifications page can help compare nearby options.
Study time depends on your background. Use a self-paced plan, review missed questions, and keep the official objectives close while you practice.
Start with a focused practice set, then use your missed questions to decide what to study next.
Official and vendor docs used to ground this page.
Explains the Prompt Lab environment for experimenting with prompts, foundation models, and prompt engineering workflows.
Explains how prompt templates are evaluated and tracked in watsonx projects.
Documents text-generation controls such as decoding and generation settings used when tuning model outputs.
Explains the AutoAI experiment workflow for automated model and pipeline creation.
Explains programmatic vector-index creation for retrieval and grounding workflows.
Explains the watsonx Tuning Studio workflow for adapting foundation models.
Explains deployment spaces used to organize and manage deployable AI assets.
Explains deployment and management of models and other AI assets in watsonx.
Describes IBM principles and practices for trustworthy and responsible AI.
Flexible search understands AI-901, ai901, ai 901, 901, ai, network plus, and saa c03.