If you're diving into generative AI and feel overwhelmed by a flood of terms such as tokenization, embeddings, RAG, fine-tuning, or agents, the Gen AI cheatsheet offers a concise roadmap. This guide gathers twenty core ideas, starting with foundational pieces like tokenization, embeddings, and attention, and advancing to sophisticated topics such as context engineering, retrieval-augmented generation (RAG), hallucination mitigation, tool usage, agent architecture, and systematic evaluation.
Key Highlights
- Tokenization fundamentals – Understand how text is broken into tokens and why large language models operate on tokens rather than raw characters.
- Fine-tuning vs. LoRA – Learn the practical differences and decide which technique fits a given scenario.
- RAG mechanics – Discover that poor RAG performance usually stems from weak retrieval components, not from the generative model itself.
- Agent design – Realize that an agent is essentially a loop that can invoke functions, enabling dynamic tool use.
- Evaluating non-deterministic AI – Follow a structured approach to assess systems whose outputs vary across runs.
- Broader concept coverage – From embeddings and attention to hallucination handling and evaluation frameworks, the cheatsheet spans the entire spectrum of modern generative AI.
The resource is especially useful for anyone preparing for interviews, seeking to systematize fragmented knowledge, or simply wanting a deeper grasp of how generative AI systems operate in practice. It condenses the most confusing jargon into clear, actionable explanations.
> Note: The author employed AI to translate the original material into Vietnamese, so some nuances or visual elements may differ from the source. If you are comfortable with English, you can view the original post at the link provided below.
Conclusion
The Gen AI cheatsheet serves as a practical reference that demystifies twenty pivotal concepts, helping professionals and learners alike navigate the rapidly evolving landscape of generative AI with confidence.
