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.

Daily Bytes of AI

Bite-sized insights for building production AI systems. Expert guides, real-world patterns, and practical engineering wisdom.

AI Bytes

Practical insights for production AI systems

Prompt Engineering

Prompt Engineering: How to Make Your LLM Think Before it Talks

Learn how to improve LLM reasoning with Chain-of-Thought (CoT) prompting. Force LLMs to show their work and solve logic problems correctly by breaking down complex tasks step-by-step.

Chain-of-Thought Reasoning

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4 min

Prompt Engineering

Prompt Engineering: How to Get Reliable, Structured JSON from an LLM

Learn how to extract structured data from LLMs using few-shot prompting, output priming, and tool calling. Build a review analyzer that outputs clean, machine-readable JSON.

Structured Output JSON

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6 min

Prompt Engineering

Prompt Engineering: From Generic Bot to Expert Agent

Learn how to transform a generic AI bot into an expert agent using explicit instructions, role prompting, and contextual prompting. Build a customer support bot step-by-step.

AI Engineering System Design

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6 min

AI Engineering in Practice

Choosing the Right LLM for Each Task: From Nano to MoE

Learn how to build asymmetric AI systems by choosing the right model for each task. Master the spectrum from Nano LLMs for routing to MoE models for generation, optimizing for speed, cost, and quality.

AI Engineering LLM System Design+1 more

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7 min

AI Engineering in Practice

AI Engineering in Practice: Building an AI Bedtime Story Generator

Learn to build a complete AI Bedtime Story Generator from scratch. A practical tutorial covering FastAPI, streaming responses, Pydantic validation, prompt engineering, and production-ready patterns for AI applications.

AI Engineering API Development Project-Based Learning+1 more

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16 min

AI Engineering

Structured Output: Making LLMs Application-Ready

Learn how to get reliable, structured outputs from LLMs using JSON mode and Pydantic tool calling. Transform LLMs from text generators into application-ready components with validated data contracts.

LLM Pydantic Tool Calling

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7 min

AI Engineering

RAG Evaluation: Proving Your System Actually Works

Learn how to evaluate RAG systems quantitatively using RAGAs framework. Measure context relevance, faithfulness, answer correctness, and prove optimizations work with A/B testing.

RAG Evaluation

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6 min

AI Engineering

Advanced RAG with Tools: Using LlamaIndex

Learn how to use LlamaIndex framework to build advanced RAG systems with tools, sub-question query engines, and ReAct agents. Build production-ready systems faster with pre-built components.

RAG AI Agents LlamaIndex

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5 min

AI Engineering

Agentic RAG: Building a System That Thinks, Acts, and Corrects

Learn how to build agentic RAG systems with tools, graphs, and self-correction. Move beyond linear chains to dynamic agents that can route, grade, and correct their own work.

RAG AI Agents System Design

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6 min

AI Engineering

Advanced RAG: Building Self-Correcting Systems

Learn how to build agentic RAG systems with routing, grading, and self-correction capabilities. Move beyond linear chains to dynamic graphs that can handle complex queries and recover from failures.

RAG AI Agents System Design

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6 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

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7 min

AI Engineering

Splitting Techniques for RAG: The Art of the Right Chunk

Master document chunking for RAG systems. Learn fixed-size, recursive, semantic, and content-aware splitting techniques to improve retrieval quality and reduce hallucinations.

RAG Vector Databases Data Processing

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6 min

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Daily Bytes of AI

AI Bytes

Prompt Engineering: How to Make Your LLM Think Before it Talks

Prompt Engineering: How to Get Reliable, Structured JSON from an LLM

Prompt Engineering: From Generic Bot to Expert Agent

Choosing the Right LLM for Each Task: From Nano to MoE

AI Engineering in Practice: Building an AI Bedtime Story Generator

Structured Output: Making LLMs Application-Ready

RAG Evaluation: Proving Your System Actually Works

Advanced RAG with Tools: Using LlamaIndex

Agentic RAG: Building a System That Thinks, Acts, and Corrects

Advanced RAG: Building Self-Correcting Systems

Vector Databases and Embeddings: The Brain of RAG

Splitting Techniques for RAG: The Art of the Right Chunk

Weekly Bytes of AI