AI Dev Skills
RAG & Knowledge
What is it?
Augmenting LLMs with retrieved context from your own knowledge base at inference time. RAG enables using proprietary data without fine-tuning and dramatically reduces hallucination.
Why it matters for AI PMs
Eliminates hallucination on domain knowledge. Enables real-time data without retraining. RAG is now the default architecture for enterprise AI — it is the solved, production-ready approach.
The 2026 landscape
Basic RAG is solved. The frontier is Advanced RAG: GraphRAG for multi-hop reasoning, hybrid search (BM25 + dense), and reranking. LlamaIndex and LangChain are the main frameworks.
What strong coverage looks like
Strong RAG coverage signals a mature knowledge management strategy. These teams have moved beyond basic similarity search to hybrid retrieval, reranking, and graph-based knowledge.
Your library coverage (0 repos)
No repos in this skill area yet.
Key concepts to know
- •Chunking strategies and overlap
- •Embedding models and vector databases
- •Hybrid search (BM25 + dense vectors)
- •Reranking with cross-encoders
- •GraphRAG for multi-hop reasoning