Certified AI Developer (CAID)

Who should get certified

CAID is intended for technical professionals who are actively involved in developing and integrating AI functionality into applications, rather than only consuming AI tools.

This certification is ideal for:

• Software engineers and full-stack developers integrating LLMs into back-end services, APIs, or web/mobile applications.
• AI and ML engineers focusing on LLM application engineering, RAG pipelines, and agentic systems.
• Data engineers and analytics engineers building LLM-assisted data workflows and retrieval layers.
• Technical product developers, solution architects, and automation engineers who design and prototype AI-powered features and services.

If you regularly write or review code and are responsible for connecting AI models to real data and systems, CAID is designed for you.

What does CAID cover

The CAID exam assesses applied, vendor-neutral skills across six domains. All questions are based on realistic development scenarios and focus on designing, building, and operating LLM-powered systems.

The exam domains are:

• LLM Foundations & AI-Assisted Development – Environment setup, fundamentals of large language models, use of AI-assisted coding tools, and secure handling of keys and secrets.
• Programmatic Prompting & Orchestration Frameworks – Structured prompting, system and user messages, tool or function calling, orchestration frameworks for multi-step workflows, validation, logging, and tracing.
• Retrieval-Augmented Generation (RAG) Systems – Designing RAG architectures, chunking strategies, embeddings and index configuration, retrieval strategies, evaluation of RAG quality, and security guardrails (for example, protection against prompt injection and data leakage).
• Local Models & Model Adaptation – Running open-weight models locally, preparing data, performing parameter-efficient fine-tuning (PEFT/LoRA or similar approaches), and evaluating adapted models against baselines.
• Agents and Tool-Using Systems – Implementing ReAct-style agents and agent graphs, multi-step planning and routing to tools or retrievers, debugging issues such as loops or incorrect tool use, and applying safety controls.
• LLMOps, Evaluation, Deployment & Governance – Monitoring and evaluation strategies, deployment patterns (such as serverless functions or containerized services), incident response, and governance, security, and compliance requirements for LLM applications.

Domain weights are balanced to ensure broad coverage of the AI development lifecycle. For the full blueprint and detailed objectives, consult the official CAID Exam Guide.

Why get certified?

Earning the CAID certification helps you demonstrate that you can move beyond experimenting with AI tools and actually build reliable, production-grade AI features.

Benefits of becoming a Certified AI Developer include:

• Demonstrate builder-level skills – Show that you can design, implement, and operate LLM-powered applications, RAG systems, and agents—not just prompt chatbots.
• Stand out for AI engineering roles – Strengthen your profile for roles such as AI application developer, AI engineer, technical product developer, or solution architect on AI-powered projects.
• Bridge research and production – Validate that you understand deployment, monitoring, evaluation, and governance concerns, not only model capabilities.
• Join a structured certification path – Position yourself within the broader AI Professional Institute certification framework, where CAID acts as a builder-focused, practitioner-level credential and a stepping stone toward future advanced certifications in AI architecture and platform engineering.

Employers can use CAID as evidence that you can build, deploy, and optimize AI solutions that other users and systems rely on.

Recommended knowledge and experience

There are no formal prerequisites for the CAID exam. However, candidates are more likely to succeed if they meet the recommended profile below.

Technical background

• Around 1–2 years of hands-on experience in a general-purpose programming language, preferably Python.
• Experience with software-engineering fundamentals such as Git workflows (branches, pull requests, code review), virtual environments and package managers, and configuring application environments.
• Basic understanding of HTTP/REST APIs, JSON, and interacting with web services.
• Practical experience calling at least one LLM via API or SDK, with awareness of context windows, sampling parameters, latency and cost trade-offs, and basic prompt engineering.

Hands-on experience

• Having built at least one LLM-based mini-project—for example:
  • a simple RAG question-answering app over private documents,
  • an internal coding assistant for a small codebase,
  • a prototype agent that calls external tools or APIs.
• Experimenting with:
  • programmatic prompting and structured outputs (such as JSON),
  • at least one orchestration framework (for example, LangChain, LangGraph, or LlamaIndex),
  • at least one vector database.

How to Get Certified?

Follow these steps to earn your CAID certification:

1. Build your skills
   Complete the AI Development and Integration course aligned with CAID, or follow a structured self-study path that covers LLM foundations, programmatic prompting, orchestration frameworks, RAG systems, local models and fine-tuning, agents, and LLMOps. Focus on hands-on labs and mini-projects that mirror real-world
2. Register for the exam
   When you are ready, schedule your online proctored CAID exam via the AIPROI Exam Portal or an AIPROI Approved Exam Provider. Choose your preferred date and time from the available slots, and carefully review the technical and identity requirements before exam day.
3. Take and pass the exam
   On exam day, complete the identity checks, follow all on-screen and proctoring instructions, and achieve at least the passing score of 720/1000 to earn your Certified AI Developer (CAID) credential. Use of AI tools during the exam is strictly prohibited under the AIPROI Exam Security Policy.
4. Maintain your certification
   Keep your CAID certification active by recertifying before it expires—either by passing the current CAID exam again or, where applicable, by earning an approved higher-level certification in the same track, according to the then-current AIPROI recertification rules.

Preparation resources

The CAID exam is closely aligned with the AI Development and Integration syllabus. To prepare effectively, we recommend that you:

• Complete the AI Development and Integration course (instructor-led or self-paced) from an AIPROI Training Partner, or an equivalent program that covers LLM foundations, programmatic prompting, orchestration frameworks, RAG, local models and fine-tuning, agents, and LLMOps.
• Build at least one end-to-end project that uses programmatic prompting and structured outputs, integrates a vector database for RAG, optionally includes an agent with tools or external APIs, and exposes a simple UI or API with basic logging and evaluation.
• Work through mini-projects such as:
  • a RAG app over private documents,
  • a local-inference lab with an open-weight model,
  • a fine-tuning lab with PEFT/LoRA,
  • an agent that orchestrates multiple tools.
• Review official documentation for at least one LLM provider, one orchestration framework, one vector database, and one local-inference tool, alongside up-to-date guides and books on AI engineering and LLM application development.
• Practice reading and reasoning about code snippets and architecture diagrams that implement LLM APIs, RAG workflows, and monitoring or security controls.

Whenever possible, prioritize understanding concepts and trade-offs over memorizing the interface of any single tool or platform.

Exam details

Key information about the CAID exam:

• Certification title: Certified AI Developer (CAID)
• Exam code: AIP-CAID-100
• Number of questions: 70 scored questions (no unscored experimental items)
• Question types: Multiple-choice (single and multiple answer), scenario-based questions, and short code-and-architecture interpretation items
• Exam duration: 120 minutes of standard testing time (additional time may be required for check-in, NDA, and instructions, depending on provider and location)
• Language: English
• Prerequisites: None (recommended knowledge and experience apply)
• Delivery method: Remote, online, proctored exam delivered via an AIPROI-approved exam platform using a secure exam browser or client
• Passing score: 720 out of 1000 on a scaled score range (0–1000)
• Scoring and reporting: Scaled scoring is used so that different exam forms remain comparable in difficulty. Candidates receive a pass/fail decision and may receive indicative domain-level feedback to highlight strengths and weaknesses.
• Certification validity: 2 years from the date of certification issue
• Retake policy (summary): Minimum 14-day waiting period between failed attempts and a maximum of three attempts in any rolling 12-month period. No retakes are permitted after a passing result, in line with the official AIPROI Exam & Retake Policy.
• Accommodations: Eligible candidates may receive time extensions or other approved accommodations in line with the AIPROI Accessibility & Accommodations Policy.

Exam availability

The CAID exam is delivered through the AIPROI exam platform and AIPROI Approved Exam Providers.

Exam availability, registration windows, and delivery options are announced via the AIPROI Exam Portal and the official certification website. Candidates can choose from available dates and time slots, subject to capacity and regional offerings.

Once you complete your registration, you will receive a confirmation email with:

• your exam date and time,
• technical and system requirements,
• instructions for identity verification and check-in,
• information about rescheduling, cancellations, and support contacts.

Candidates should carefully review all communications from the exam provider and ensure they meet the technical and identity requirements before the exam day.