What's the time commitment for this bootcamp?

The bootcamp requires 10 hours per week over 6 weeks. This includes live sessions, hands-on projects, and self-paced learning. Most students find this manageable alongside their full-time jobs.

Do I need prior AI experience to join?

No prior AI experience is required, but you should have few years of software development experience. The bootcamp is designed for software engineers who want to upskill in AI engineering.

What if I can't make a live session?

All live sessions are recorded and available for replay. We also offer multiple office hours throughout the week, so you can catch up on any missed content or get help with assignments.

How much should I budget for APIs and resources?

We estimate €10-50 for the entire bootcamp, covering API costs for OpenAI and other services. We'll show you how to optimize costs and use free tiers when possible.

What happens if I can't attend this cohort?

You can defer to the next cohort at no additional cost. We run cohorts every 2-3 months, so you won't have to wait long to join.

How long will I have access to materials after the bootcamp?

You'll have lifetime access to all course materials, recordings, and the private community. This includes future updates and new content we add to the bootcamp.

What's the refund policy?

Yes, you can get a 100% refund if you've progressed less than 10% of the bootcamp or it's within 7 days of your purchase. We're confident in our curriculum and instructor quality, which is why we offer this guarantee.

Do you offer team discounts?

Yes! We offer 20%+ discounts for teams of 3 or more. Contact us at param@learnwithparam.com for team pricing and bulk enrollment options.

Embeddings • Daily Bytes of AI

Master embeddings with 3 bite-sized engineering insights covering production strategies, implementation techniques, and real-world best practices.

AI Engineering in Practice

The 'Brain' of RAG: A Guide to Embeddings & Vector Databases

Understand how embeddings and vector databases work under the hood. Learn how computers translate text meaning into numbers and search millions of documents instantly using ANN algorithms.

RAG Vector Databases Embeddings

Read Article

9 min

AI Engineering

Vector Databases and Embeddings: The Brain of RAG

Learn how embeddings and vector databases power RAG systems. Understand semantic search, cosine similarity, metadata filtering, and choose between open-source and proprietary embedding models.

RAG Vector Databases Embeddings

Read Article

7 min

AI Engineering

Retrieval-Augmented Generation (RAG): Giving LLMs an Open Book

Learn how RAG solves LLM limitations by connecting models to external documents. Master chunking, embeddings, vector databases, and the retrieve-augment-generate pipeline for production AI systems.

RAG Vector Databases Embeddings

Read Article

8 min

AI Engineering Bootcamp

Master Production AI Systems in 6 Weeks

Learn to build, deploy, and scale production-ready AI applications with hands-on projects and expert guidance.

6 Weeks Intensive

Live Sessions

Expert Mentors

Learn More