Topic
RAG
Retrieval-augmented generation looks up relevant documents and gives them to a model before it answers, so the answer rests on your data rather than the model's memory. It is the most common way to put AI over company knowledge. Results depend mostly on the retrieval, meaning how content is split, indexed and ranked, not on the model.
2 guides on RAG.
Keep learning RAG
- Building RAG Applications · LangChain video · pixegami
- Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents video · IBM Technology
- Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!! video · StatQuest with Josh Starmer
- Is Fine-Tuning Still Needed? LLMs, RAG, & LoRA video · IBM Technology
- New course: Building Agentic RAG with LlamaIndex video · DeepLearningAI
- RAG Explained in 5 Minutes · IBM video · IBM Technology
- RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models video · IBM Technology
- RAG's Evolution: From Simple Retrieval to Agentic AI video · IBM Technology
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