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.

31 AI Bytes

Practical insights for production AI systems

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

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9 min

AI Engineering

How to Choose Your Vector Database

Compare vector databases for RAG systems. Learn when to use Chroma, Qdrant, Weaviate, pgvector, Pinecone, and Vespa based on performance, scale, and developer experience.

RAG Vector Databases System Design

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12 min

AI Engineering

Batteries-Included RAG Platforms: Dify vs. RAGFlow vs. Onyx

Compare batteries-included RAG platforms that bundle the entire pipeline into one application. Learn when to use Dify, RAGFlow, and Onyx for rapid deployment vs. build-it-yourself frameworks.

RAG System Design LLMOps

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9 min

AI Engineering

Choosing Your RAG Framework: LangChain vs. LlamaIndex vs. Haystack

Demystify RAG frameworks by understanding their core philosophies. Learn when to use LangChain, LlamaIndex, and Haystack for building production-ready retrieval-augmented generation systems.

RAG System Design LangChain+2 more

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8 min

AI Engineering

Choosing Your AI Agent Framework: LangGraph vs. LlamaIndex vs. CrewAI vs. AutoGen

Demystify AI agent frameworks by understanding their core philosophies. Learn when to use LangGraph, LlamaIndex, CrewAI, and AutoGen for single-agent vs. multi-agent systems.

AI Agents System Design LangGraph+3 more

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11 min

Prompt Engineering

Prompt Engineering: The Coherent Author (An Autonomous Writing Agent)

Learn how to build long-form writing agents that maintain coherence using Meta-Prompting and Thread-of-Thought techniques. Create autonomous agents that write structured, consistent content.

AI Agents Long-Form Writing Meta-Prompting

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5 min

Prompt Engineering

Prompt Engineering: Giving Your Bot Tools to See the World

Learn how to build ReAct agents that can use tools to access real-time data. Transform closed-book AI into agents that can reason, act, and observe using external tools.

AI Agents ReAct Tools

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5 min

Prompt Engineering

Prompt Engineering: The Refinement Loop (How to Make an LLM Critique Itself)

Learn how to improve LLM output quality using Self-Critique and Refinement techniques. Force LLMs to review and improve their own work through iterative refinement loops.

Self-Critique Refinement

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4 min

...

Daily Bytes of AI

31 AI Bytes

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

How to Choose Your Vector Database

Batteries-Included RAG Platforms: Dify vs. RAGFlow vs. Onyx

Choosing Your RAG Framework: LangChain vs. LlamaIndex vs. Haystack

Choosing Your AI Agent Framework: LangGraph vs. LlamaIndex vs. CrewAI vs. AutoGen

Prompt Engineering: The Coherent Author (An Autonomous Writing Agent)

Prompt Engineering: Giving Your Bot Tools to See the World

Prompt Engineering: The Refinement Loop (How to Make an LLM Critique Itself)

Weekly Bytes of AI — Newsletter by Param