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ANVISoftware Solutions

AI Application Developer

Beginner50-70 hours

This path is for building AI features into software, not for training models. It starts with accurate mental models — what a language model does and does not do — because most failed AI projects begin with a wrong assumption rather than a coding mistake.

You then build: call a model from code, get structured output you can validate, connect it to your own documents through retrieval, and add the evaluation and guardrails that separate a demo from something operable.

The path treats model output as untrusted input throughout. That single discipline prevents most of the problems AI features run into.

Prerequisites

  • Ability to program in at least one language
  • Understanding of HTTP APIs

The route

Take these in order. Each step exists because the previous one left something unresolved.

  1. AI & Generative AI for Developers

    Foundations, prompting, retrieval, agents, evaluation and cost.

  2. Python and FastAPI as needed

    Enough Python to build the orchestration service, if you do not already have it.

    Checkpoint — practice, not a course

  3. Security for Developers

    Prompt injection and data boundaries make this more relevant than it first appears.

  4. Docker & Containers

    Package the AI service for deployment.

  5. Build the knowledge assistant

    Retrieval over a document set, with evaluation and guardrails in place.

    Checkpoint — practice, not a course

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Have a project worth talking through?

Tell us what you're building or what's slowing your current system down. We'll give you a direct read on scope and approach.