How Agents Pull In Outside Knowledge (RAG)

Why this matters

Retrieval-Augmented Generation is the dominant way agents pull external knowledge at query time — understanding how embeddings, vector search, and re-ranking interact tells you exactly what content properties increase the chance your product surfaces in an agent's context window.

Try it yourself

1. Take two descriptions of your product — one fluffy marketing copy, one factual feature list — embed both using any free embedding API and measure their cosine similarity to a query like 'best tool for X' — record which scores higher.
2. Inspect a publicly documented RAG pipeline (LlamaIndex or LangChain docs) and locate the top-k parameter — change it from 3 to 8 in the config and predict how that affects latency and answer quality.
3. Identify one specific sentence in your product's documentation that is a strong candidate for retrieval — explain why in terms of semantic density (information per token).

References

• https://arxiv.org/abs/2005.11401